Systems and methods for particle analysis

ABSTRACT

The present disclosure provides systems and methods for sorting a cell. The system may comprise a flow channel configured to transport a cell through the channel. The system may comprise an imaging device configured to capture an image of the cell from a plurality of different angles as the cell is transported through the flow channel. The system may comprise a processor configured to analyze the image using a deep learning algorithm to enable sorting of the cell.

CROSS-REFERENCE

This application claims the benefit of U.S. Patent Application No.62/764,965, filed Aug. 15, 2018, which is entirely incorporated hereinby reference.

BACKGROUND

Cell physical and morphological properties can be used to study celltype and cell state and to diagnose diseases. Cell shape is one of themarkers of cell cycle. Eukaryotic cells show physical changes in shapewhich can be cell-cycle dependent, such as a yeast cell undergoingbudding or fission. Shape is also an indicator of cell state and canbecome an indicator used for clinical diagnostics. Blood cell shape maychange due to many clinical conditions, diseases, and medications, suchas the changes in red cells' morphologies resulting from parasiticinfections. Other parameters such as features of cell membrane,nuclear-to-cytoplasm ratio, nuclear envelope morphology, and chromatinstructure can also be used to identify cell type and disease state. Inblood, for instance, different cell types are distinguished by factorssuch as cell size, cell shape, and nuclear shape.

Biologists and cytopathologists use cell size and morphology to identifycell type and diagnose disease. This is mainly done by some sort ofmicroscopic imaging and manual analysis of the images. As a result, theexisting methods are time consuming, subjective, qualitative, and proneto error. Cytopathologists, for instance, review slides prepared fromdifferent tissues using a light microscope and look for features thatresemble characteristics of disease. This process is time-consuming andthe results are subjective and may be impacted by factors such as theorientation of the stained cells, how the slide was prepared, and theexpertise of the cytopathologists. Although there have been recentefforts to automate the analysis of cytology smears, there are stillchallenges. One of the main problems with the analysis of the smears isthe existence of contaminant cells that are hard to avoid and make itdifficult to detect rare cells or specific feature characteristics ofdisease. Other issues are the angles of the stained or smeared cells,which can obscure essential information for identification of a celltype or state. As such, there remains a need for improved methods and/orsystems for cell analysis.

SUMMARY

In an aspect, the present disclosure provides a cell sorting systemcomprising: a flow channel configured to transport a cell through thechannel; an imaging device configured to capture an image of the cellfrom a plurality of different angles as the cell is transported throughthe flow channel; and a processor configured to analyze the image usinga deep learning algorithm to enable sorting of the cell.

In some embodiments, a width or a height of the flow channel isnon-uniform along an axis of the flow channel. In some embodiments, thewidth or the height of the flow channel gradually increases along adirection of the flow channel through which the cell is transported.

In some embodiments, the flow channel comprises walls that are formed tofocus the cell into a streamline. In some embodiments, the system isconfigured to focus the cell into the streamline using inertial liftforces or hydrodynamic forces. In some embodiments, the system isfurther configured to focus the cell at a height within the flowchannel. In some embodiments, the system is configured to rotate thecell within the streamline. In some embodiments, the flow channelcomprises a square, rectangular, round, or half-ellipsoid cross-section.

In some embodiments, the plurality of angles extend around the cell orover a portion of the cell.

In some embodiments, the image comprises a plurality of images capturedfrom the plurality of angles, wherein the plurality of images comprise:(1) an image captured from a top side of the cell, (2) an image capturedfrom a bottom side of the cell, (3) an image captured from a front sideof the cell, (4) an image captured from a rear side of the cell, (5) animage captured from a left side of the cell, or (6) an image capturedfrom a right side of the cell.

In some embodiments, the image comprises a two-dimensional image or athree-dimensional image.

In some embodiments, the flow channel is configured to transport aplurality of cells through the flow channel, wherein the plurality ofcells comprise the cell, and wherein the imaging device is configured tocapture a plurality of images of the plurality of cells from a pluralityof different angles relative to each of the plurality of cells.

In some embodiments, the imaging device is configured to capture theplurality of images onto a single image frame.

In some embodiments, the flow channel branches into a plurality ofchannels, and the system is configured to sort the cell by directing thecell to a selected channel of the plurality of channels based on theanalyzed image.

In some embodiments, the system further comprises a laser-validationmodule configured to detect the cell after the cell has been sorted.

In another aspect, the present disclosure provides a method of sorting acell, the method comprising: transporting a cell through a flow channel;capturing an image of the cell from a plurality of different angles asthe cell is transported through the flow channel; and analyzing theimage using a deep learning algorithm to sort the cell.

In some embodiments, the method further comprises rotating the cell asthe cell is being transported through the flow channel. In someembodiments, the method further comprises focusing the cell into astreamline at a height within the flow channel as the cell is beingtransported through the flow channel.

In some embodiments, a plurality of images comprising the image arecaptured at a rate of about 10 frames per second to about 500,000 framesper second.

In some embodiments, the plurality of angles extend around the cell orover a portion of the cell.

In some embodiments, capturing the image of the cell comprises capturinga plurality of images from (1) a top side of the cell, (2) a bottom sideof the cell, (3) a front side of the cell, (4) a rear side of the cell,(5) a left side of the cell, or (6) a right side of the cell.

In some embodiments, the method further comprises sorting the cell basedon the analyzed image, by directing the cell to a selected channel of aplurality of channels downstream of the flow channel. In someembodiments, the plurality of channels excluding the selected channelare closed prior to directing the cell to the selected channel. In someembodiments, the plurality of channels excluding the selected channelare closed using pressure, an electric field, a magnetic field, or acombination thereof. In some embodiments, the method further comprisesvalidating the sorting of the cell using a laser.

In some embodiments, the method further comprises sorting a plurality ofcells at a rate of at least 10 cells per second, wherein the pluralityof cells comprises the cell.

In some embodiments, the method further comprises: sorting a pluralityof cells including the cell using a classifier; and feeding data fromthe sorting back to the classifier in order to train the classifier forfuture sorting. In some embodiments, the classifier comprises a neuralnetwork. In some embodiments, the classifier is configured to performclassification of each of the plurality of cells, based onclassification probabilities corresponding to a plurality of analyzedimages of the plurality of cells.

In some embodiments, the cell is from a biological sample of a subject,and wherein the method further comprises determining a presence or anabsence of a condition or an attribute in the subject based on theanalyzed image.

In a different aspect, the present disclosure provides a computerprogram product comprising a non-transitory computer-readable mediumhaving computer-executable code encoded therein, the computer-executablecode adapted to be executed to implement the method of sorting the cell.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications, and NCBI accessionnumbers mentioned in this specification are herein incorporated byreference to the same extent as if each individual publication, patent,patent application, or NCBI accession number was specifically andindividually indicated to be incorporated by reference. To the extentpublications and patents, patent applications, or NCBI accession numbersincorporated by reference contradict the disclosure contained in thespecification, the specification is intended to supersede and/or takeprecedence over any such contradictory material.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the disclosure are set forth with particularity inthe appended claims. A better understanding of the features andadvantages of the present disclosure will be obtained by reference tothe following detailed description that sets forth illustrativeembodiments, in which the principles of the disclosure are utilized, andthe accompanying drawings (also “Figure” and “FIG.” herein), of which:

FIG. 1A conceptually illustrates a classification and/or sorting systemin accordance with one embodiment of the disclosure.

FIG. 1B conceptually illustrates a microfluidic design of a flow cell inaccordance with one embodiment of the disclosure.

FIG. 2 conceptually illustrates an exemplary long flow channel withcontrolled length to ensure that the objects in the system arrive at thebifurcation exactly when the decision is made and valve actuation iscompleted.

FIG. 3 conceptually illustrates an exemplary three-branch channeldesign.

FIG. 4 conceptually illustrates a particle rotating as it moves throughthe channel.

FIG. 5 conceptually illustrates an out-of-focus cell and an in-focuscell using inertia-based z focusing.

FIG. 6 conceptually illustrates a non-limiting triple-punch design.

FIG. 7 conceptually illustrates the adaptive labeling framework.

FIG. 8 conceptually illustrates the modifications made to Resnet50,wherein early layers are elongated in exchange of shrinkage oflate-stage layers, in order to enhance it and improve its accuracy.

FIG. 9 conceptually illustrates the multi-view ensemble inferenceframework.

FIG. 10 conceptually illustrates the multi-view to multi-channel dataframework.

FIG. 11 conceptually illustrates a non-limiting sorting design.

FIG. 12 conceptually illustrates a non-limiting sorting design that usespiezo actuator.

FIG. 13 conceptually illustrates a non-limiting sorting design that usesmagnetic actuation.

FIG. 14 conceptually illustrates a technique wherein the two lasers fromthe system of the present disclosure are recombined with a closeproximity.

FIG. 15 conceptually illustrates a validation technique that comprises abackchannel design.

FIG. 16 shows a computer system that is programmed or otherwiseconfigured to implement methods provided herein.

DETAILED DESCRIPTION

While various embodiments of the disclosure have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. Numerousvariations, changes, and substitutions may occur to those skilled in theart without departing from the disclosure. It should be understood thatvarious alternatives to the embodiments of the disclosure describedherein may be employed.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which the present disclosure belongs. In case of conflict,the present application including the definitions will control. Also,unless otherwise required by context, singular terms shall includepluralities and plural terms shall include the singular.

Classification and/or Sorting Systems

In an aspect, the present disclosure describes a cell sorting system.The cell sorting system can comprise a flow channel configured totransport a cell through the channel. The cell sorting system cancomprise an imaging device configured to capture an image of the cellfrom a plurality of different angles as the cell is transported throughthe flow channel. The cell sorting system can comprise a processorconfigured to analyze the image using a deep learning algorithm toenable sorting of the cell. The cell sorting system can be a cellclassification system. In some cases, the flow channel can be configuredto transport a solvent (e.g., liquid, water, media, alcohol, etc.)without any cell. The cell sorting system can have one or moremechanisms (e.g., a motor) for moving the imaging device relative to thechannel. Such movement can be relative movement, and thus the movingpiece can be the imaging device, the channel, or both. The processor canbe further configured to control such relative movement.

In some embodiments, the disclosure provides a classification and/orsorting system as illustrated in FIG. 1A shows a schematic illustrationof the cell sorting system with a flow cell design (e.g., a microfluidicdesign), with further details illustrated in FIG. 1B. In operation, asample 102 is prepared and injected by a pump 104 (e.g., a syringe pump)into a flow cell 106, or flow-through device. In some embodiments, theflow cell 106 is a microfluidic device. Although FIG. 1A illustrates aclassification and/or sorting system utilizing a syringe pump, any of anumber of perfusion systems can be used such as (but not limited to)gravity feeds, peristalsis, or any of a number of pressure systems. Insome embodiments, the sample is prepared by fixation and staining. Insome examples, the sample comprises live cells. As can readily beappreciated, the specific manner in which the sample is prepared islargely dependent upon the requirements of a specific application.

In some embodiments, a cell suspension sample is prepared atconcentrations ranging between 1×10⁴-5×10⁶ cells/mL. In someembodiments, a cell suspension sample is prepared at concentrationsranging between 1×10⁴-5×10⁴ cells/mL. In some embodiments, a cellsuspension sample is prepared at concentrations ranging between5×10⁴-1×10⁵ cells/mL. In some embodiments, a cell suspension sample isprepared at concentrations ranging between 1×10⁵-5×10⁵ cells/mL. In someembodiments, a cell suspension sample is prepared at concentrationsranging between 5×10⁵-1×10⁶ cells/mL. In some embodiments, a cellsuspension sample is prepared at concentrations ranging between1×10⁶-5×10⁶ cells/mL. In some embodiments, a cell suspension sample isprepared at concentrations ranging between 5×10⁴-5×10⁵ cells/mL. In someembodiments, a cell suspension sample is prepared at concentrationsranging between 1×10⁴-5×10⁵ cells/mL. In some embodiments, a cellsuspension sample is prepared at concentrations ranging between5×10⁴-1×10⁶ cells/mL. In some embodiments, a cell suspension sample isprepared at concentrations ranging between 2×10⁵-5×10⁵ cells/mL. In someembodiments, a cell suspension sample is prepared at concentrationsranging between 3×10⁵-5×10⁵ cells/mL. In some embodiments, a cellsuspension sample is prepared at concentrations ranging between4×10⁵-5×10⁵ cells/mL. In some embodiments, a cell suspension sample isprepared at concentrations ranging between 1×10⁵-4×10⁵ cells/mL. In someembodiments, a cell suspension sample is prepared at concentrationsranging between 2×10⁵-4×10⁵ cells/mL. In some embodiments, a cellsuspension sample is prepared at concentrations ranging between3×10⁵-4×10⁵ cells/mL. In some embodiments, a cell suspension sample isprepared at concentrations ranging between 1×10⁵-3×10⁵ cells/mL. In someembodiments, a cell suspension sample is prepared at concentrationsranging between 2×10⁵-3×10⁵ cells/mL. In some embodiments, a cellsuspension sample is prepared at concentrations ranging between1×10⁵-2×10⁵ cells/mL.

In some embodiments, a cell suspension sample is prepared at aconcentration of about 1×10⁴ cells/mL, about 5×10⁴ cells/mL, about 1×10⁵cells/mL, about 2×10⁵ cells/mL, about 3×10⁵ cells/mL, about 4×10⁵cells/mL, about 5×10⁴ cells/mL, about 5×10⁵ cells/mL, about 1×10⁶cells/mL, or about 5×10⁶ cells/mL.

The specific concentration utilized in a given classification and/orsorting system typically depends upon the capabilities of the system.Cells may be fixed and stained with colored dyes (e.g., Papanicolaou andWright Giemsa methods). Classification and/or sorting systems inaccordance with various embodiments of the disclosure can operate withlive, fixed and/or Wright Giemsa-stained cells. Staining can helpincrease the contrast of nuclear organelles and improve classificationaccuracy. In some embodiments the cells in the sample are not labelledand/or stained. After preparation, the cell suspension sample can beinjected into the microfluidic device using a conduit such as (but notlimited to) tubing and a perfusion system such as (but not limited to) asyringe pump.

In some embodiments, a syringe pump injects the sample at about 10μL/min. In some embodiments, a syringe pump injects the sample at about50 μL/min. In some embodiments, a syringe pump injects the sample atabout 100 μL/min. In some embodiments, a syringe pump injects the sampleat about 150 μL/min. In some embodiments, a syringe pump injects thesample at about 200 μL/min. In some embodiments, a syringe pump injectsthe sample at about 250 μL/min. In some embodiments, a syringe pumpinjects the sample at about 500 μL/min. In some embodiments, a syringepump injects the sample at about 10 μL/min to about 500 μL/min, forexample at about 10 μL/min to about 50 μL/min, about 10 μL/min to about100 μL/min, about 10 μL/min to about 150 μL/min, about 10 μL/min toabout 200 μL/min, about 10 μL/min to about 250 μL/min, about 10 μL/minto about 300 μL/min, about 10 μL/min to about 350 μL/min, about 10μL/min to about 400 μL/min, or about 10 μL/min to about 450 μL/min.

As can readily be appreciated, any perfusion system, including but notlimited to peristalsis systems and gravity feeds, appropriate to a givenclassification and/or sorting system can be utilized.

As noted above, the flow cell 106 can be implemented as a fluidic devicethat focuses cells from the sample into a single streamline that isimaged continuously. In the illustrated embodiment, the cell line isilluminated by a light source 108 and an optical system 110 that directslight onto an imaging region 138 of the flow cell 106. An objective lenssystem 112 magnifies the cells by directing light toward the sensor of ahigh-speed camera system 114.

In some embodiments, a 10×, 20×, 40×, 60×, 80×, 100×, or 200× objectiveis used to magnify the cells. In some embodiments, a 10×, objective isused to magnify the cells. In some embodiments, a 20× objective is usedto magnify the cells. In some embodiments, a 40× objective is used tomagnify the cells. In some embodiments, a 60× objective is used tomagnify the cells. In some embodiments, a 80× objective is used tomagnify the cells. In some embodiments, a 100× objective is used tomagnify the cells. In some embodiments, a 200× objective is used tomagnify the cells. In some embodiments, a 10× to a 200× objective isused to magnify the cells, for example a 10×-20×, a 10×-40×, a 10×-60×,a 10×-80×, or a 10×-100× objective is used to magnify the cells.

As can readily be appreciated by a person having ordinary skill in theart, the specific magnification utilized can vary greatly and is largelydependent upon the requirements of a given imaging system and cell typesof interest.

In some embodiments, one or more imaging devices (e.g., at least 1, 2,3, 4, 5, or more imaging devices) may be used to capture images of thecell. In some aspects, the imaging device is a high-speed camera. Insome aspects, the imaging device is a high-speed camera with amicrosecond exposure time. In some instances, said exposure time is 1millisecond. In some instances, said exposure time is between 1millisecond (ms) and 0.75 millisecond. In some instances, said exposuretime is between 1 ms and 0.50 ms. In some instances, said exposure timeis between 1 ms and 0.25 ms. In some instances, said exposure time isbetween 0.75 ms and 0.50 ms. In some instances, said exposure time isbetween 0.75 ms and 0.25 ms. In some instances, said exposure time isbetween 0.50 ms and 0.25 ms. In some instances, said exposure time isbetween 0.25 ms and 0.1 ms. In some instances, said exposure time isbetween 0.1 ms and 0.01 ms. In some instances, said exposure time isbetween 0.1 ms and 0.001 ms. In some instances, said exposure time isbetween 0.1 ms and 1 microsecond (μs). In some aspects, said exposuretime is between 1 μs and 0.1 μs. In some aspects, said exposure time isbetween 1 μs and 0.01 μs. In some aspects, said exposure time is between0.1 μs and 0.01 μs. In some aspects, said exposure time is between 1 μsand 0.001 μs. In some aspects, said exposure time is between 0.1 μs and0.001 μs. In some aspects, said exposure time is between 0.01 μs and0.001 μs.

In some embodiments, image sequences from cells are recorded at rates ofabout 10 frames/sec to about 10,000,000 frames/sec. In some embodiments,image sequences from cells are recorded at rates of at least about 10frames/sec. In some embodiments, image sequences from cells are recordedat rates of at most about 10,000,000 frames/sec. In some embodiments,image sequences from cells are recorded at rates of about 10 frames/secto about 100 frames/sec, about 10 frames/sec to about 1,000 frames/sec,about 10 frames/sec to about 10,000 frames/sec, about 10 frames/sec toabout 100,000 frames/sec, about 10 frames/sec to about 1,000,000frames/sec, about 10 frames/sec to about 10,000,000 frames/sec, about100 frames/sec to about 1,000 frames/sec, about 100 frames/sec to about10,000 frames/sec, about 100 frames/sec to about 100,000 frames/sec,about 100 frames/sec to about 1,000,000 frames/sec, about 100 frames/secto about 10,000,000 frames/sec, about 1,000 frames/sec to about 10,000frames/sec, about 1,000 frames/sec to about 100,000 frames/sec, about1,000 frames/sec to about 1,000,000 frames/sec, about 1,000 frames/secto about 10,000,000 frames/sec, about 10,000 frames/sec to about 100,000frames/sec, about 10,000 frames/sec to about 1,000,000 frames/sec, about10,000 frames/sec to about 10,000,000 frames/sec, about 100,000frames/sec to about 1,000,000 frames/sec, about 100,000 frames/sec toabout 10,000,000 frames/sec, or about 1,000,000 frames/sec to about10,000,000 frames/sec. In some embodiments, image sequences from cellsare recorded at rates of about 10 frames/sec, about 100 frames/sec,about 1,000 frames/sec, about 10,000 frames/sec, about 100,000frames/sec, about 1,000,000 frames/sec, or about 10,000,000 frames/sec.

In some embodiments, image sequences from cells are recorded at rates ofbetween 10,000-10,000,000 frames/sec using a high-speed camera, whichmay be color, monochrome, and/or imaged using any of a variety ofimaging modalities including (but not limited to) the near-infraredspectrum. In some embodiments, image sequences from cells are recordedat rates of between 50,000-5,000,000 frames/sec. In some embodiments,image sequences from cells are recorded at rates of between50,000-100,000 frames/sec. In some embodiments, image sequences fromcells are recorded at rates of between 100,000-1,000,000 frames/sec. Insome embodiments, image sequences from cells are recorded at rates ofbetween 100,000-500,000 frames/sec. In some embodiments, image sequencesfrom cells are recorded at rates of between 500,000-1,000,000frames/sec. In some embodiments, image sequences from cells are recordedat rates of between 1,000,000-5,000,000 frames/sec.

In some embodiments, image sequences from cells are recorded at a rateof about 50,000 frames/sec, about 100,000 frames/sec, about 200,000frames/sec, about 300,000 frames/sec, about 400,000 frames/sec, about500,000 frames/sec, about 750,000 frames/sec, about 1,000,000frames/sec, about 2,500,000 frames/sec, about 5,000,000 frames/sec, orabout 10,000,000 frames/sec.

In some embodiments, the imaging device used in the present disclosureis an ultra-high speed camera, wherein images are recorded at a rate ofup to 25,000,000 frames/sec. In some instances, said ultra-high speedcamera runs at 20,000 revolutions per second. In some instances, saidhigh-speed camera has a resolution of 616×920 pixels.

The imaging device(s) (e.g., the high-speed camera) of the imagingsystem can comprise an electromagnetic radiation sensor (e.g., IRsensor, color sensor, etc.) that detects at least a portion of theelectromagnetic radiation that is reflected by and/or transmitted fromthe flow cell or any content (e.g., the cell) in the flow cell. Theimaging device can be in operative communication with one or moresources (e.g., at least 1, 2, 3, 4, 5, or more) of the electromagneticradiation. The electromagnetic radiation can comprise one or morewavelengths from the electromagnetic spectrum including, but not limitedto x-rays (about 0.1 nanometers (nm) to about 10.0 nm; or about 10¹⁸Hertz (Hz) to about 10¹⁶ Hz), ultraviolet (UV) rays (about 10.0 nm toabout 380 nm; or about 8×10¹⁶ Hz to about 10¹⁵ Hz), visible light (about380 nm to about 750 nm; or about 8×10¹⁴ Hz to about 4×10¹⁴ Hz), infrared(IR) light (about 750 nm to about 0.1 centimeters (cm); or about 4×10¹⁴Hz to about 5×10¹¹ Hz), and microwaves (about 0.1 cm to about 100 cm; orabout 10⁸ Hz to about 5×10¹¹ Hz). In some cases, the source(s) of theelectromagnetic radiation can be ambient light, and thus the cellsorting system may not have an additional source of the electromagneticradiation.

The imaging device(s) can be configured to take a two-dimensional image(e.g., one or more pixels) of the cell and/or a three-dimensional image(e.g., one or more voxels) of the cell.

In some embodiment, the imaging area is illuminated with a high-powerlight-emitting diode (LED) with exposure times that is less than 1millisecond (msec) to help prevent motion blurring of cells. In someembodiment, the imaging area is illuminated with a high-power LED withexposure times that is less than 1 microsecond (μsec) to help preventmotion blurring of cells. In some embodiments the imaging devicecomprises a combination of a strobe light and a camera. Strobe light,strobe, stroboscopic lamp, and strobing light may be usedinterchangeably. In some instances, a strobe light is a device used toproduce regular flashes of light. In some embodiments, a high-speedstrobe light is used in combination with one or more cameras. In someinstances, said high-speed strobe lights are capable of up to 2500strobes per second. In some instances, said high-speed strobe lights arecapable of up to 5000 strobes per second. In some instances, saidhigh-speed strobe lights have a storage of electrical energy to pulsethe LEDs wherein said energy can go up to 2000 watts when the LEDs areactive. In some instances, said high-speed strobe light pulses the LEDwith up to 180 amps of DC current. In some instances, said strobe lightis white. In some instances, said strobe light is blue with a wavelengthof 470 nm. In some instances, said strobe light is green with awavelength of 530 nm. In some instances, said strobe light is red with awavelength of 625 nm. In some instances, said strobe light is infraredwith a wavelength of 850 nm. In some embodiments, the imaging devicecomprises a combination of a strobe light and one or more cameraswherein said cameras are high-speed camera. In some embodiments, theimaging device comprises a combination of a high-speed strobe light andone or more cameras, wherein said cameras are high-speed cameras.

As can readily be appreciated, the exposure times can differ acrossdifferent systems and can largely be dependent upon the requirements ofa given application or the limitations of a given system such as but notlimited to flow rates. Images are acquired and can be analyzed using animage analysis algorithm.

In some embodiments, the images are acquired and analyzed post-capture.In some aspects, the images are acquired and analyzed in real-timecontinuously. Using object tracking software, single cells can bedetected and tracked while in the field of view of the camera.Background subtraction can then be performed. In a number ofembodiments, the flow cell 106 causes the cells to rotate as they areimaged and multiple images of each cell are provided to a computingsystem 116 for analysis. In some embodiments, the multiple imagescomprise images from a plurality of cell angles.

The flow rate and channel dimensions can be determined to obtainmultiple images of the same cell from a plurality of different angles(i.e., a plurality of cell angles). A degree of rotation between anangle to the next angle may be uniform or non-uniform. In some examples,a full 360° view of the cell is captured. In some embodiments, 4 imagesare provided in which the cell rotates 90° between successive frames. Insome embodiments, 8 images are provided in which the cell rotates 45°between successive frames. In some embodiments, 24 images are providedin which the cell rotates 15° between successive frames. In someembodiments, at least three or more images are provided in which thecell rotates at a first angle between a first frame and a second frame,and the cell rotates at a second angle between the second frame and athird frame, wherein the first and second angles are different.

The cell can have a plurality sides. The plurality of sides of the cellcan be defined with respect to a direction of the transport (flow) ofthe cell through the channel. In some cases, the cell can comprise astop side, a bottom side that is opposite the top side, a front side(e.g., the side towards the direction of the flow of the cell), a rearside opposite the front side, a left side, and/or a right side oppositethe left side. In some cases, the image of the cell can comprise aplurality of images captured from the plurality of angles, wherein theplurality of images comprise: (1) an image captured from the top side ofthe cell, (2) an image captured from the bottom side of the cell, (3) animage captured from the front side of the cell, (4) an image capturedfrom the rear side of the cell, (5) an image captured from the left sideof the cell, and/or (6) an image captured from the right side of thecell

In some embodiments, a two-dimensional “hologram” of a cell can begenerated by superimposing the multiple images of the individual cell.The “hologram” can be analyzed to automatically classify characteristicsof the cell based upon features including but not limited to themorphological features of the cell.

In some embodiments, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 images arecaptured for each cell. In some embodiments, 5 or more images arecaptured for each cell. In some embodiments, from 5 to 10 images arecaptured for each cell. In some embodiments, 10 or more images arecaptured for each cell. In some embodiments, from 10 to 20 images arecaptured for each cell. In some embodiments, 20 or more images arecaptured for each cell. In some embodiments, from 20 to 50 images arecaptured for each cell. In some embodiments, 50 or more images arecaptured for each cell. In some embodiments, from 50 to 100 images arecaptured for each cell. In some embodiments, 100 or more images arecaptured for each cell.

In some embodiments, the imaging device is moved so as to capturemultiple images of the cell from a plurality of angles. In some aspects,said images are captured at an angle between 0 and 90 degrees to thehorizontal axis. In some aspects, said images are captured at an anglebetween 90 and 180 degrees to the horizontal axis. In some aspects, saidimages are captured at an angle between 180 and 270 degrees to thehorizontal axis. In some aspects, said images are captured at an anglebetween 270 and 360 degrees to the horizontal axis.

In some embodiments, multiple imaging devices (for e.g. multiplecameras) are used wherein each device captures an image of the cell froma specific cell angle. In some aspects, 2, 3, 4, 5, 6, 7, 8, 9, or 10cameras are used. In some aspects, more than 10 cameras are used,wherein each camera images the cell from a specific cell angle,

As can readily be appreciated, the number of images that are captured isdependent upon the requirements of a given application or thelimitations of a given system. In several embodiments, the flow cell hasdifferent regions to focus, order, and/or rotate cells. Although thefocusing regions, ordering regions, and cell rotating regions arediscussed as affecting the sample in a specific sequence, a personhaving ordinary skill in the art would appreciate that the variousregions can be arranged differently, where the focusing, ordering,and/or rotating of the cells in the sample can be performed in anyorder. Regions within a microfluidic device implemented in accordancewith an embodiment of the disclosure are illustrated in FIG. 1B. Flowcell 106 may include a filtration region 130 to prevent channel cloggingby aggregates/debris or dust particles. Cells pass through a focusingregion 132 that focuses the cells into a single streamline of cells thatare then spaced by an ordering region 134. In some embodiments, thefocusing region utilizes “inertial focusing” to form the singlestreamline of cells. In some embodiments, the focusing region utilizes‘hydrodynamic focusing” to focus the cells into the single streamline ofcells. Optionally, prior to imaging, rotation can be imparted upon thecells by a rotation region 136. The optionally spinning cells can thenpass through an imaging region 138 in which the cells are illuminatedfor imaging prior to exiting the flow cell. These various regions aredescribed and discussed in further detail below.

In some embodiments, a single cell is imaged in a field of view of theimaging device, e.g. camera. In some embodiments, multiple cells areimaged in the same field of view of the imaging device. In some aspects,1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 cells are imaged in the same field ofview of the imaging device. In some aspects, up to 100 cells are imagedin the same field of view of the imaging device. In some instances, 10to 100 cells are imaged in said field of view, for example, 10 to 20cells, 10 to 30 cells, 10 to 40 cells, 10 to 50 cells, 10 to 60 cells,10 to 80 cells, 10 to 90 cells, 20 to 30 cells, 20 to 40 cells, 20 to 50cells, 20 to 60 cells, 20 to 70 cells, 20 to 80 cells, 20 to 90 cells,30 to 40 cells, 40 to 50 cells, 40 to 60 cells, 40 to 70 cells, 40 to 80cells, 40 to 90 cells, 50 to 60 cells, 50 to 70 cells, 50 to 80 cells,50 to 90 cells, 60 to 70 cells, 60 to 80 cells, 60 to 90 cells, 70 to 80cells, 70 to 90 cells, 90 to 100 cells are imaged in the same field ofview of the imaging device.

In some cases, only a single cell may be allowed to be transportedacross a cross-section of the flow channel perpendicular to the axis ofthe flow channel. In some cases, a plurality of cells (e.g., at least 2,3, 4, 5, or more cells) may be allowed to be transported simultaneouslyacross the cross-section of the flow channel perpendicular to the axisof the flow channel. In such a case, the imaging device (or theprocessor operatively linked to the imaging device) may be configured totrack each of the plurality of cells as they are transported along theflow channel.

In some embodiments, the classification and/or sorting systems inaccordance with various embodiments of the disclosure eliminate thevariability involved in manual preparation of slides, which rely onexpertise of the operator. Furthermore, image segmentation can beavoided. The classification and/or sorting system allows for high flowrates and high-throughputs can be achieved.

In some embodiments, the classification and/or sorting system includesan imaging system that can capture images at a rate of at least 100cells/second and a computing system that can classify at a rate of atleast 100 cells/second. In some embodiments, the classification and/orsorting system includes an imaging system that can capture images at arate of at least 500 cells/second and a computing system that canclassify at a rate of at least 500 cells/second. In some embodiments,the classification and/or sorting system includes an imaging system thatcan capture images at a rate of at least 1000 cells/second and acomputing system that can classify at a rate of at least 1000cells/second. In some embodiments, the classification and/or sortingsystem includes an imaging system that can capture images at a rate ofat least 2000 cells/second and a computing system that can classify at arate of at least 2000 cells/second. In some embodiments, theclassification and/or sorting system includes an imaging system that cancapture images at a rate of at least 5000 cells/second and a computingsystem that can classify at a rate of at least 5000 cells/second. Insome embodiments, the classification and/or sorting system includes animaging system that can capture images at a rate of at least 10,000cells/second and a computing system that can classify at a rate of atleast 10,000 cells/second.

In some embodiments, the classification and/or sorting system includesan imaging system that can capture images at a rate that is equal up to100 cells/second and a computing system that can classify up to 100cells/second. In some embodiments, the classification and/or sortingsystem includes an imaging system that can capture images at a rate thatis equal up to 500 cells/second and a computing system that can classifyup to 500 cells/second. In some embodiments, the classification and/orsorting system includes an imaging system that can capture images at arate that is equal to up to 1000 cells/second and a computing systemthat can classify up to 1000 cells/second. In some embodiments, theclassification and/or sorting system includes an imaging system that cancapture images at a rate that is equal to up to 2000 cells/second and acomputing system that can classify up to 2000 cells/second. In someembodiments, the classification and/or sorting system includes animaging system that can capture images at a rate that is equal to up to5000 cells/second and a computing system that can classify up to 5000cells/second. In some embodiments, the classification and/or sortingsystem includes an imaging system that can capture images at a rate thatis equal to up to 10,000 cells/second and a computing system that canclassify up to 10,000 cells/second.

The imaging system can include, among other things, a camera, anobjective lens system and a light source. In a number of embodiments,flow cells similar to those described above can be fabricated usingstandard 2D microfluidic fabrication techniques, requiring minimalfabrication time and cost.

Although specific classification and/or sorting systems, flow cells, andmicrofluidic devices are described above with respect to FIGS. 1A and1B, classification and/or sorting systems can be implemented in any of avariety of ways appropriate to the requirements of specific applicationsin accordance with various embodiments of the disclosure. Specificelements of microfluidic devices that can be utilized in classificationand/or sorting systems in accordance with some embodiments of thedisclosure are discussed further below.

Microfluidic Device Fabrication

Microfluidic devices in accordance with several embodiments of thedisclosure can be fabricated using a variety of methods. In someembodiments, a combination of photolithography and mold casting is usedto fabricate a microfluidic device. Conventional photolithographytypically involves the use of photoresist and patterned light to createa mold containing a positive relief of the desired microfluidic patternon top of a substrate, typically a silicon wafer. Photoresist is aphoto-curable material that can be used in photolithography to createstructures with feature sizes on the order of micrometers (μm). Duringfabrication, the photoresist can be deposited onto a substrate. Thesubstrate can be spun to create a layer of photoresist with a targeteddesired height. The photoresist layer can then be exposed to light,typically UV light (depending on the type of photoresist), through apatterned mask to create a cured pattern of photoresist. The remaininguncured portions can be developed away, leaving behind a positive reliefmold that can be used to fabricate microfluidic devices.

From the mold, material can be cast to create a layer containing anegative relief pattern. Inlet and outlet holes can be formed atappropriate regions, and the device can then be bonded to a backing tocreate a flow-through device, or flow cell, with flow channels (e.g.,microfluidic channels). A cross-section of the flow channel can have awidth and a height. The cross-section may be perpendicular to an axis ofthe flow channel (e.g., a direction of the flow of the solvent with orwithout cells inside the flow channel). The width and the height of theflow channel can be perpendicular to each other. The width and/or theheight of the flow channel can be uniform or non-uniform along the axisof the flow channel. In some cases, the width or the height of the flowchannel can increase or decrease (e.g., gradually or rapidly) along adirection of the flow channel through which the cell is transported. Insome cases, the width or the height of the flow channel can increasealong a section of the flow channel, and decrease along a differentsection of the flow channel.

The width or the height of the cross-section of the flow channel can beabout 1 μm to about 500 μm. The width or the height of the cross-sectionof the flow channel can be at least about 1 μm, 2 μm, 3 μm, 4 μm, 5 μm,6 μm, 7 μm, 8 μm, 9 μm, 10 μm, 20 μm, 30 μm, 40 μm, 50 μm, 60 μm, 70 μm,80 μm, 90 μm, 100 μm, 200 μm, 300 μm, 400 μm, 500 μm, or more. The widthor the height of the cross-section of the flow channel can be at mostabout 500 μm, 400 μm, 300 μm, 200 μm, 100 μm, 90 μm, 0 μm, 0 μm, 60 μm,50 μm, 40 μm, 30 μm, 20 μm, 10 μm, 9 μm, 8 μm, 7 μm, 6 μm, 5 μm, 4 μm, 3μm, 2 μm, 1 μm, or less.

The width or the height of the channel can increase or decrease alongthe direction of the flow channel at about 0.01 percent per μm (%/μm) toabout 1000%/μm. The increase of decrease of the width or height of thechannel along the direction of the flow channel can be at least about0.01%/μm, 0.05%/μm, 0.1%/μm, 0.5%/μm, 1%/μm, 5%/μm, 10%/μm, 50%/μm,100%/μm, 500%/μm, 1000%/μm, or more. The increase of decrease of thewidth or height of the channel along the direction of the flow channelcan be at most about 1000%/μm, 500%/μm, 100%/μm, 50%/μm, 10%/μm, 5%/μm,1%/μm, 0.5%/μm, 0.1%/μm, 0.05%/μm, 0.01%/μm, or less.

In some embodiments, the system of the present disclosure comprisesstraight channels with rectangular or square cross-sections. In someaspects, the system of the present disclosure comprises straightchannels with round cross-sections. In some aspects, said systemcomprises straight channels with half-ellipsoid cross-sections. In someaspects, said system comprises spiral channels. In some aspects, saidsystem comprises round channels with rectangular cross-sections. In someaspects, said system comprises round channels with rectangular channelswith round cross-sections. In some aspects, said system comprises roundchannels with half-ellipsoid cross-sections. In some aspects, saidsystem comprises channels that are expanding and contracting in widthwith rectangular cross-sections. In some aspects, said system compriseschannels that are expanding and contracting in width with roundcross-sections. In some aspects, said system comprises channels that areexpanding and contracting in width with half-ellipsoid cross-sections.

In some embodiments utilizing a rotation section, a two-layerfabrication process can be used to orient the rotation section so thatimaging of the cells as they rotate will provide images of cells atdifferent angles with a more accurate representation of cellularfeatures.

As can be readily appreciated, the microfluidic device can be fabricatedusing a variety of materials as appropriate to the requirements of thegiven application. In imaging applications, the microfluidic device istypically made of an optically transparent material such as (but notlimited to) polydimethylsiloxane (PDMS). In some embodiments, themicrofluidic device is made of silicon. In some embodiments, themicrofluidic device is made of low-temperature cofired ceramic (LTCC).In some embodiments, the microfluidic device is made of thermosetpolyester (TPE). In some embodiments, the microfluidic device is made ofpolystyrene (PS). In some embodiments, the microfluidic device is madeof polycarbonate (PC). In some embodiments, the microfluidic device ismade of poly-methyl methacrylate (PMMA). In some embodiments, themicrofluidic device is made of poly(ethylene glycol) diacrylate (PEGDA).In some embodiments, the microfluidic device is made ofpolyfluoropolyether diol methacrylate (PFPE-DMA). In some embodiments,the microfluidic device is made of polyurethane (PU).

Although a specific method of microfluidic device fabrication isdiscussed, any of a variety of methods can be implemented to fabricate amicrofluidic device utilized in accordance with various embodiments ofthe disclosure as appropriate to the requirements of a givenapplication.

Microfluidic Filters

Microfluidic devices in accordance with several embodiments of thedisclosure can include one or more microfluidic filters at the inlets,or further down, of the microfluidic device to prevent channel clogging.In other embodiments, filtration can occur off device. The specificdimensions and patterns of the filters and the microfluidic channel canvary and are largely dependent upon the sizes of the cells of interestand the requirements of a given application. Any of a variety ofmicrofluidic filter systems can be implemented on microfluidic devicesutilized in accordance with various embodiments of the disclosure asappropriate to the requirements of a given flow application.

Focusing Regions

The flow channel can comprise one or more walls that are formed to focusone or more cells into a streamline. The flow channel can comprise afocusing region comprising the wall(s) to focus the cell(s) into thestreamline. Focusing regions on a microfluidic device can take adisorderly stream of cells and utilize a variety of forces (for e.g.inertial lift forces (wall effect and shear gradient forces) orhydrodynamic forces) to focus the cells within the flow into astreamline of cells. In some embodiments, the cells are focused in asingle streamline. In some examples, the cells are focused in multiplestreamlines, for example at least 2, at least 3, at least 4, at least 5,at least 6, at least 7, at least 8, at least 9, or at least 10streamlines.

The focusing region receives a flow of randomly arranged cells via anupstream section. The cells flow into a region of contracted andexpanded sections in which the randomly arranged cells are focused intoa single streamline of cells. The focusing is driven by the action ofinertial lift forces (wall effect and shear gradient forces) acting oncells at Reynolds numbers>1, where channel Reynolds number is defined asfollows: Re_(c)=ρU_(m)W/μ, where U_(m) is the maximum fluid velocity, ρis the fluid density, μ is the fluid viscosity, and W is the channeldimension. In some embodiments, Reynolds numbers around 20-30 can beused to focus particles from about 10 μm to about 20 μm. In someembodiments, the Reynolds number is such that laminar flow occurs withinthe microfluidic channels. As can readily be appreciated, the specificchannel Reynolds number can vary and is largely determined by thecharacteristics of the cells for which the microfluidic device isdesigned, the dimensions of the microfluidic channels, and the flow ratecontrolled by the perfusion system.

In some embodiments, the focusing region is formed with curvilinearwalls that form periodic patterns. In some embodiments, the patternsform a series of square expansions and contractions. In otherembodiments, the patterns are sinusoidal. In further embodiments, thesinusoidal patterns are skewed to form an asymmetric pattern. Thefocusing region can be effective in focusing cells over a wide range offlow rates. In the illustrated embodiment, an asymmetricalsinusoidal-like structure is used as opposed to square expansions andcontractions. This helps prevent the formation of secondary vortices andsecondary flows behind the particle flow stream. In this way, theillustrated structure allows for faster and more accurate focusing ofcells to a single lateral equilibrium position. Spiral and curvedchannels can also be used in an inertia regime; however, these cancomplicate the integration with other modules. Finally, straightchannels where channel width is greater than channel height can also beused for focusing cells onto single lateral position. However, in thiscase, since there will be more than one equilibrium position in thez-plane, imaging can become problematic, as the imaging focal plane ispreferably fixed. As can readily be appreciated, any of a variety ofstructures that provide a cross section that expands and contracts alongthe length of the microfluidic channel or are capable of focusing thecells can be utilized as appropriate to the requirements of specificapplications.

The cell sorting system can be configured to focus the cell at a widthand/or a height within the flow channel along an axis of the flowchannel. The cell can be focused to a center or off the center of thecross-section of the flow channel. The cell can be focused to a side(e.g., a wall) of the cross-section of the flow channel. A focusedposition of the cell within the cross-section of the channel may beuniform or non-uniform as the cell is transported through the channel.

While specific implementations of focusing regions within microfluidicchannels are described above, any of a variety of channel configurationsthat focus cells into a single streamline can be utilized as appropriateto the requirements of a specific application in accordance with variousembodiments of the disclosure.

Ordering Regions

Microfluidic channels can be designed to impose ordering upon a singlestreamline of cells formed by a focusing region in accordance withseveral embodiments of the disclosure. Microfluidic channels inaccordance with some embodiments of the disclosure include an orderingregion having pinching regions and curved channels. The ordering regionorders the cells and distances single cells from each other tofacilitate imaging. In some embodiments, ordering is achieved by formingthe microfluidic channel to apply inertial lift forces and Dean dragforces on the cells. Dean flow is the rotational flow caused by fluidinertia. The microfluidic channel can be formed to create secondaryflows that apply a Dean drag force proportional to the velocity of thesecondary flows. Dean drag force scales with about ρU_(m) ²αD_(h) ²/R,where ρ is the fluid density, Um is the maximum fluid velocity,

D_(h)=2WH/(W+H) is the channel hydraulic diameter, α is the particledimension, and R is the curvature radius. The force balance betweeninertial lift and Dean drag forces determines particle equilibriumposition.

Depending on the particle size, the relative interior and exterior radiiof curvature (R_(lin,out)) of the channel and channel height (H_(C)) ofthe microfluidic channel can be determined to reach equilibrium atdesired locations. Different combinations of curved and pinching regions(and their parameters) can be used to achieve desired distance betweenparticles. Channel width in the pinching region can be adjusted suchthat the cells will not be squeezed through the channels, causingpossible damage to the cell membrane (the cells can, however, beslightly deformed without touching the channel walls while travelingthrough the pinching regions). Additionally, the squeezing could causedebris/residues from cell membrane left on the channel walls, which willchange the properties of the channel. The ordering in the pinchingregions is driven by instantaneous change in channel fluidic resistanceupon arrival of a cell/particle. Since the channel width in this regionis close to cell/particle dimensions, when a cell arrives at thepinching region, the channel resistance increases. Since the wholesystem is pressure-regulated (constant pressure), this can cause aninstantaneous decrease in flow rate and therefore spacing of the cells.The length and width of pinching region can be adjusted to reach desiredspacing between cells. The curved channel structure can also help withfocusing cells to a single z position, facilitating imaging.

Different geometries, orders, and/or combinations can be used. In someembodiments, pinching regions can be placed downstream from the focusingchannels without the use of curved channels. Adding the curved channelshelps with more rapid and controlled ordering, as well as increasing thelikelihood that particles follow a single lateral position as theymigrate downstream. As can readily be appreciated, the specificconfiguration of an ordering region is largely determined based upon therequirements of a given application.

Cell Rotating Regions

Microfluidic channels can be configured to impart rotation on orderedcells in accordance with a number of embodiments of the disclosure. Cellrotation regions of microfluidic channels in accordance with someembodiments of the disclosure use co-flow of a particle-free buffer toinduce cell rotation by using the co-flow to apply differential velocitygradients across the cells. In several embodiments, the cell rotationregion of the microfluidic channel is fabricated using a two-layerfabrication process so that the axis of rotation is perpendicular to theaxis of cell downstream migration and parallel to cell lateralmigration. Cells are imaged in this region while tumbling and rotatingas they migrate downstream. This allows for the imaging of a cell atdifferent angles, which provides more accurate information concerningcellular features than can be captured in a single image or a sequenceof images of a cell that is not rotating to any significant extent. Thisalso allows for a 3D reconstruction of the cell using available softwaresince the angles of rotation across the images are known. In someembodiments, a similar change in velocity gradient across the cell isachieved by providing a change in channel height (i.e. the dimensionthat is the smaller of the two dimensions of the cross section of themicrofluidic channel and the dimension perpendicular to the imagingplane). This increase in channel height should be such that the widthcontinues to be greater than the height of the channel. Also in the caseof increasing channel height, there can be a shift in cell focusingposition in the height dimension, which should be accounted for duringimaging and adjustment of the imaging focal plane.

In some embodiments, a cell rotation region of a microfluidic channelincorporates an injected co-flow prior to an imaging region inaccordance with an embodiment of the disclosure. Co-flow may beintroduced in the z plane (perpendicular to the imaging plane) to spinthe cells. Since the imaging is done in the x-y plane, rotation of cellsaround an axis parallel to the y-axis provides additional information byrotating portions of the cell that may have been occluded in previousimages into view in each subsequent image. Due to a change in channeldimensions, at point x₀, a velocity gradient is applied across thecells, which can cause the cells to spin. The angular velocity of thecells depends on channel and cell dimensions and the ratio between Q1(main channel flow rate) and Q2 (co-flow flow rate) and can beconfigured as appropriate to the requirements of a given application. Insome embodiments, a cell rotation region incorporates an increase in onedimension of the microfluidic channel to initiate a change in thevelocity gradient across a cell to impart rotation onto the cell. Insome aspects, a cell rotation region of a microfluidic channelincorporates an increase in the z-axis dimension of the cross section ofthe microfluidic channel prior to an imaging region in accordance withan embodiment of the disclosure. The change in channel height caninitiate a change in velocity gradient across the cell in the z axis ofthe microfluidic channel, which can cause the cells to rotate as withusing co-flow.

Although specific techniques for imparting velocity gradients upon cellsare described above, any of a variety of techniques can be utilized toimpart rotation on a single streamline of cells as appropriate to therequirements of specific applications in accordance with variousembodiments of the disclosure.

Flowing Cells

In some embodiments, the system and methods of the present disclosurefocuses the cells in microfluidic channels. The term focusing as usedherein broadly means controlling the trajectory of cell/cells movementand comprises controlling the position and/or speed at which the cellstravel within the microfluidic channels. In some embodiments controllingthe lateral position and/or the speed at which the particles travelinside the microfluidic channels, allows to accurately predict the timeof arrival of the cell at a bifurcation. The cells may then beaccurately sorted. The parameters critical to the focusing of cellswithin the microfluidic channels include, but are not limited to channelgeometry, particle size, overall system throughput, sampleconcentration, imaging throughput, size of field of view, and method ofsorting.

In some embodiments the focusing is achieved using inertial forces. Insome embodiments, the system and methods of the present disclosure focuscells to a certain height from the bottom of the channel using inertialfocusing (Dino Di Carlo, 2009, Lab on a Chip). In these embodiments, thedistance of the cells from the objective is equal and images of all thecells will be clear. As such, cellular details, such as nuclear shape,structure, and size appear clearly in the outputted images with minimalblur. In some aspects, the system disclosed herein has an imagingfocusing plane that is adjustable. In some aspects, the focusing planeis adjusted by moving the objective or the stage. In some aspects, thebest focusing plane is found by recording videos at different planes andthe plane wherein the imaged cells have the highest Fourier magnitude,thus, the highest level of detail and highest resolution, is the bestplane (FIG. 5).

In some embodiments, the system and methods of the present disclosureutilize a hydrodynamic-based z focusing system to obtain a consistent zheight for the cells of interests that are to be imaged. In someaspects, said design comprises hydrodynamic focusing using multipleinlets for main flow and side flow. In some aspects, saidhydrodynamic-based z focusing system is a triple-punch design (FIG. 6).In some aspects, said design comprises hydrodynamic focusing with threeinlets, wherein the two side flows pinch cells at the center. Forcertain channel designs, dual z focus points may be created, wherein adouble-punch design similar to the triple-punch design may be used tosend objects to one of the two focus points to get consistent focusedimages. In some aspects, said design comprises hydrodynamic focusingwith 2 inlets, wherein only one side flow channel is used and cells arefocused near channel wall. In some aspects, said hydrodynamic focusingcomprises side flows that do not contain any cells and a middle inletthat contains cells. The ratio of the flow rate on the side channel tothe flow rate on the main channel determines the width of cell focusingregion. In some aspects, said design is a combination of the above. Inall aspects, said design is integrable with the bifurcation and sortingmechanisms disclosed herein. In some aspects, said hydrodynamic-based zfocusing system is used in conjunction with inertia-based z focusing.

In some embodiments, the terms “particles”, “objects”, and “cells” areused interchangeably. In some aspects, said cell is a live cell. In someaspects, said cell is a fixed cell.

Method to Control Particle Arrival Times

In various embodiments, the systems and methods of the presentdisclosure sort the cells by ensuring that the cells in the systemarrive at the bifurcation exactly when the decision is made and valveactuation is completed. In some aspects, ensuring that the objectsarrive at the bifurcation exactly when the decision is made and thevalve actuation is completed is achieved by controlling length of themicrofluidic channels. In some embodiments the channel is a long channeland the length of the channel is determined based on factors thatinclude, but are not limited to, (i) the estimated time of decisionmaking, (ii) latency and opening window of switching mechanism, such asvalves, and (iii) velocity of the particles (FIG. 2).

In some embodiments, ensuring that the objects arrive at the bifurcationexactly when the decision is made and the valve actuation is completedis achieved by controlling the velocity of the cells. In some examples,this is achieved by branching the microfluidic channels. In theseexamples one or more branches are used to guide the fluid into one ormore of multiple channels, thereby dropping the velocity of fluid andparticles in any one channel while keeping particles in focus. In someaspects, the factor by which the velocity is dropped depends onparameters including but limited to number of channels, relative widthof channels, and/or relative height of channels. An exemplary snapshotof a 3-branch design with particles focused to center is shown in (FIG.3). In some embodiments the microfluidic channel is divided into 2, 3,4, 5, 6, 7, 8, 9, or 10 branches.

In some embodiments, ensuring that the particles arrive at thebifurcation exactly when the decision is made and the valve actuation iscompleted is achieved by a gradual increase in width and/or height ofthe channel. In these examples, the velocity of particles can bedecreased in a controlled manner guiding the particles into a widerand/or taller channel. In some embodiments, the angle and/or length ofexpansion are designed to avoid particles from changing trajectory. Thiswill prevent the particles from getting out of focus.

In some embodiments, ensuring that the particles arrive at thebifurcation exactly when the decision is made and the valve actuation iscompleted is achieved by designs within the microfluidic channels. Suchdesigns include but are not limited to curved and/or spiral designs thatcan delay particles before they arrive at bifurcation, while keepingtheir lateral positions as well as their relative longitudinal positionto each other constant and controlled.

In some aspects, the methods and the systems disclosed herein ensurethat the particles arrive at the bifurcation exactly when the decisionis made and valve actuation is completed, as well as ensure that thelateral position of the particles upon their arrival is controlled.

Imaging and Classification

A variety of techniques can be utilized to classify images of cellscaptured by classification and/or sorting systems in accordance withvarious embodiments of the disclosure. In some embodiments, the imagecaptures are saved for future analysis/classification either manually orby image analysis software. Any suitable image analysis software can beused for image analysis. In some embodiments, image analysis isperformed using OpenCV. In some embodiments, analysis and classificationis performed in real time. In some embodiments images are captured atframe rates between 10-10,000,000 frames per second, for example between10 frames/sec to about 100 frames/sec, about 10 frames/sec to about1,000 frames/sec, about 10 frames/sec to about 10,000 frames/sec, about10 frames/sec to about 100,000 frames/sec, about 10 frames/sec to about1,000,000 frames/sec, about 10 frames/sec to about 10,000,000frames/sec, about 100 frames/sec to about 1,000 frames/sec, about 100frames/sec to about 10,000 frames/sec, about 100 frames/sec to about100,000 frames/sec, about 100 frames/sec to about 1,000,000 frames/sec,about 100 frames/sec to about 10,000,000 frames/sec, about 1,000frames/sec to about 10,000 frames/sec, about 1,000 frames/sec to about100,000 frames/sec, about 1,000 frames/sec to about 1,000,000frames/sec, about 1,000 frames/sec to about 10,000,000 frames/sec, about10,000 frames/sec to about 100,000 frames/sec, about 10,000 frames/secto about 1,000,000 frames/sec, about 10,000 frames/sec to about10,000,000 frames/sec, about 100,000 frames/sec to about 1,000,000frames/sec, about 100,000 frames/sec to about 10,000,000 frames/sec, orabout 1,000,000 frames/sec to about 10,000,000 frames/sec andclassification is performed in real time. In some embodiments images arecaptured at frame rates between 100,000 and 500,000 frames per secondand classification is performed in real time. In some aspects, imagesare captured at frame rates between 200,000 and 500,000 frames persecond and classification is performed in real time. In some aspects,images are captured at frame rates between 300,000 and 500,000 framesper second and classification is performed in real time. In someaspects, images are captured at frame rates between 300,000 and 500,000frames per second and classification is performed in real time. In someaspects, images are captured at frame rates between 400,000 and 500,000frames per second and classification is performed in real time.

In some embodiments, the objects placed in the system disclosed hereinflow at very high speeds through the channels of said system in order tomaintain a high throughput and highly effective inertia focusing. Insome aspects, the system of the present disclosure comprises a veryhigh-speed camera with microsecond exposure time to decrease blur inoutputted images. In some aspects, said high-speed camera capturesimages at frame rates between 200,000 and 500,000 frames per second. Insome aspects, said high-speed camera captures images at frame ratesbetween 300,000 and 500,000 frames per second. In some aspects, saidhigh-speed camera captures images at frame rates between 300,000 and500,000 frames per second. In some aspects, said high-speed cameracaptures images at frame rates between 400,000 and 500,000 frames persecond. In some aspects, said system comprises a high-speed strobinglight. In some aspects, said system comprises a high-speed strobinglight with a slower camera, wherein multiple snapshots of the sameobject may be imaged onto one image frame (e.g., a single image frame),which may be and separated later with the feature extraction andclassification algorithm disclosed herein.

In some embodiments, the system and methods of the present disclosurecomprise collecting a plurality of images of objects in the flow. Insome aspects, said plurality of images comprises at least 20 images ofcells. In some aspects, said plurality of images comprises at least 19,18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, or 2 images ofcells. In some embodiments, the plurality of images comprises imagesfrom multiple cell angles. In some aspects, said plurality of images,comprising images from multiple cell angles, help derive extra featuresfrom the particle which would typically be hidden if the particle isimaged from a single point-of-view. In some examples, the microfluidicsystem, wherein said system forces result in the particle rotating as itmoves through the microfluidic channel (FIG. 4).

In some embodiments, the systems and methods of present disclosure allowfor a tracking ability, wherein said system and methods track a particle(e.g., cell) under the camera and maintain the knowledge of which framesbelong to the same particle. In some embodiments, the particle istracked until it has been classified and/or sorted.

In some embodiments, the systems and methods of the disclosure compriseimaging a single particle in a particular field of view of the camera.In some aspects, the system and methods of the present disclosure imagemultiple particles in the same field of view of camera. Imaging multipleparticles in the same field of view of the camera can provide additionaladvantages, for example it will increase the throughput of the system bybatching the data collection and transmission of multiple particles. Insome instances, about 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60,70, 80, 90, or 100 particles are imaged in the same field of view of thecamera. In some instances, 100 to 200 particles are imaged in the samefield of view of the camera

Feature Extraction and Classification

In some embodiments, the analysis of the images and/or classification ofthe particles are performed manually, for example by a human being. Insome embodiments, the classification is performed by one or moreclassifiers. In some embodiments, the classifier is an automatedclassifier. In some embodiments, the classifier is trained to performoutcome determination based on cell feature and/or pattern recognition.The cell features and/or cell patterns include, but are not limited to,recognition of cell shape, cell diameter, nuclear shape, nucleardiameter, nuclear texture, nuclear edges, nuclear area, nuclear averageintensity, nucleus to cytoplasm ratio, cell texture, cell edges, cellarea, cell average intensity, DNA content (pgDNA) of the cells and/orthe like.

In some embodiments, the one or more classifiers used in the system andmethods of the present disclosure are trained using an adaptive labelingapproach. In such training approach a classifier is first trained with afirst set of known cells. The classifier is then allowed to classifycells in a second set of cells, such that the classifier classifies eachcell in the second set of cells with a classification probability,wherein the classification probability is a representation of theclassifier's certainty about the classification of a particular cell.After the classification of the second set of cells, cells classifiedwith a classification probability lower than a predefined threshold areselected. These selected cells will represent cases where the classifieris “unsure” about the class. In some cases, this uncertainty may be dueto sparsity of that particular type of cells in the first set of knowncells. Once, the cells classified with a lower than the pre-definedthreshold have been selected, the classifier is trained in a next roundto classify these selected cells. In some embodiments, these steps canbe repeated till a majority of cells, for e.g. greater than 50% ofcells, greater than 55% of cells, greater than 60% of cells, greaterthan 65% of cells, greater than 70% of cells, greater than 75% of cells,greater than 80% of cells, greater than 85% of cells, greater than 90%of cells, greater than 91% of cells, greater than 91% of cells, greaterthan 92% of cells, greater than 93% of cells, greater than 94% of cells,greater than 95% of cells, greater than 96% of cells, greater than 97%of cells, greater than 98% of cells, greater than 99% of cells, or 100%of cells are classified with the classification probability equal to orgreater than a predefined threshold. The technique is outlined in FIG.7.

In some examples, the predefined threshold is a classificationprobability of greater than 0.50. In some examples, the predefinedthreshold is a classification probability that is greater than 0.60. Insome examples, the predefined threshold is a classification probabilitythat is greater than 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.91, 0.92,0.93, 0.94, 0.95, 0.96, 0.97, 0.98, or greater than 0.99. In someinstances, the threshold probability is a classification probabilityof 1. In various embodiments, the classification probability iscalculated based on validation tests. The validations tests can rangefrom biological validations to software and simulation validations.

In some embodiments, the systems and methods disclosed herein useenhanced neural network designed specifically for cell imageclassification, for e.g. for single cell image classification.Accordingly, provided herein is a general framework that modifies aconvolution neural architecture for single cell image classification. Insome aspects, said enhanced neural network is designed by modificationof known neural networks designed to work well with ordinary images,such as the image-net. In some aspects the enhanced neural networks aredesigned by the modification of neural networks such as AlexNet, VGGNet,GoogLeNet, ResNet (residual networks), DenseNet, and Inception networks.In some examples, the enhanced neural networks are designed bymodification of ResNet (e.g. ResNet 18, ResNet 34, ResNet 50, ResNet101, and ResNet 152) or inception networks. In some aspects, themodification comprises a series of network surgery operations that aremainly carried out to improve including inference time and/or inferenceaccuracy.

In some embodiments the modification of the known neural networkscomprises shrinking one or more late stage layers of the known neuralnetwork. In some example, shrinking the one or more late stage layersresults in improved inference time of the neural network. In someinstances, later-stage layers play less important roles in cell imagedata due to the visual features in cell images having lower complexitythan objects found in image-net set or similar datasets.

In some aspects, shrinkage of late-stage layers may result in anaccuracy loss. In some embodiments, such loss may be compensated atleast partially by the addition of back hidden layers to earlier partsof the network. In some examples, shrinking the late stage layers andadding them to an earlier part of the neural network improves accuracyof the classifier even more than keeping them in the late layers.

In various examples, the early vs. late layers are distinguished bytheir distance to the input layer and/or

An exemplary enhanced neural network designed by the methods disclosedherein is shown in FIG. 8. The exemplary enhanced neural network isdesigned by modification of a residual learning network known as ResNet50. In the modification exemplified, one block of 512 depth and twoblocks of 256 depth, both of which are late stage residual layers areremoved, and one block is added to the 128 block, which comes earlier inthe network. In this particular example, the removal of late stageblocks improved the inference time, and the addition of the 128 depthblock gained back and even improved the inference accuracy which wasinitially feasible with the standard ResNet 50.

In some embodiments, the use of the enhanced neural networks asdisclosed herein improves the inference time, wherein the increase inthe inference time is calculated as a percent increase using theformula: Percentage increase in the inference time=(inference time usingthe enhanced neural network-inference time using the known neuralnetwork)/inference time using the known neural network X100. In someembodiments, the use of the enhanced neural networks as disclosed hereinimproves the inference time by at least 10% as compared to the inferencetime with the known network the enhanced network is derived from. Insome examples, the inference time is improved by at least 20%, at least30%, at least 40%, at least 50%, at least 60%, at least 70%, at least80%, at least 90%, at least 100%, at least 150%, at least 200%, at least250%, at least 300%, at least 350%, at least 400%, at least 450%, atleast 500%. In some embodiments, the inference accuracy of the enhancedneural networks is improved by at least 10%, at least 20%, at least 30%,at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, atleast 90%, at least 100%, at least 150%, at least 200%, at least 250%,at least 300%, at least 350%, at least 400%, at least 450%, at least500%.

In some embodiments, the accuracy of the system and/or methods disclosedherein is improved by using multiple images belonging to the same cellto assist and increase the accuracy of a classifier. FIG. 9 illustratesthis embodiment. In some embodiments, the multiple images compriseimages obtained from multiple cell angles. In some embodiments, theimages from the multiple cell angles are captured as the cell isrotation in the microfluidic channel. In some embodiments, the imagesfrom multiple cell angles are captured by moving one or more imagingdevices as the particle passes through the microfluidic channel. In someembodiments, the images from multiple cell angles are captured bymultiple imaging devices positioned to capture particle images fromspecific particle angles.

In some embodiments, the accuracy of the systems and methods disclosedherein is further improved by utilizing ensemble methods forclassification. The ensemble methods disclosed herein use a set ofclassifiers which have been trained based on different neural networks,with different initial conditions, etc. The set of classifiers are thenallowed to classify the particle and the results from each of theclassifier in the set of classifiers are aggregated to determine a finalclassification of the particle. In some embodiments at least 2, at least3, at least 4, at least 5, at least 6, at least 7, at least 8, at least9, or at least 10 classifiers are used.

In some embodiments, the system and methods of the present disclosureleverage the multi-view nature of the acquired data by concatenatingimages obtained from multiple cell angles in a single training orinference procedure to the classifier. In some aspects, saidconcatenating is similar to that carried out in traditional neural netsto concatenate red green blue (RGB) colors. In some aspects, the presentdisclosure concatenates separate images belonging to the same cell orparticle, wherein both training and classification procedures are spedup since only one inference operation is carried out per cell. FIG. 10illustrates this embodiment.

Sorting

In some embodiments, the systems and the methods of the presentdisclosure actively sorts a stream of particles. The term sort orsorting as used herein refers to physically separating particles, fore.g. cells, with one or more desired characteristics. The desiredcharacteristic(s) can comprise a feature of the cell(s) analyzed and/orobtained from the image(s) of the cell. Examples of the feature of thecell(s) can comprise a size, shape, volume, electromagnetic radiationabsorbance and/or transmittance (e.g., fluorescence intensity,luminescence intensity, etc.), or viability (e.g., when live cells areused).

The flow channel can branch into a plurality of channels, and the cellsorting system can be configured to sort the cell by directing the cellto a selected channel of the plurality of channels based on the analyzedimage of the cell. The analyzed image may be indicative of one or morefeatures of the cell, wherein the feature(s) are used as parameters ofcell sorting. In some cases, one or more channels of the plurality ofchannels can have a plurality of sub-channels, and the plurality ofsub-channels can be used to further sort the cells that have been sortedonce.

In some aspects, the systems and methods disclosed herein use an activesorting mechanism. In various embodiments, the active sorting isindependent from analysis and decision making platforms and methods. Invarious embodiments the sorting is performed by a sorter, which receivesa signal from the decision making unit (e.g. a classifier), or any otherexternal unit, and then sorts cells as they arrive at the bifurcation.The term bifurcation as used herein refers to the termination of theflow channel into two or more channels, such that cells with the one ormore desired characteristics are sorted or directed towards one of thetwo or more channels and cell without the one or more desiredcharacteristics are directed towards the remaining channels. In someembodiments, the flow channel terminates into 2, 3, 4, 5, 6, 7, 8, 9, 10or more channels. In some embodiments, the flow channel terminates intwo channels and cells with one or more desired characteristics aredirected towards one of the two channels (the positive channel), whilecells without the one or more desired characteristics are directedtowards the other channel (the negative channel). In some embodiments,the flow channel terminates in three channels and cells with a firstdesired characteristic are directed to one of the three channels, cellswith a second desired characteristic are directed to another of thethree channels, and cells without the first desired characteristic andthe second desired characteristic are directed to the remaining of thethree channels.

In some embodiments, the sorting is performed by a sorter. The sortermay function by predicting the exact time at which the particle willarrive at the bifurcation. To predict the time of particle arrival, thesorter can use any applicable method. In some examples, said sorterpredicts the time of arrival of the particle by using velocity ofparticles that are upstream and the distance between velocitycalculation location and bifurcation. In some examples, said sorterpredicts the time of arrival of the particles by using a constant delaytime as an input.

In some examples, said sorter predicts the time of arrival of theparticles by using a self-included unit which is capable of detectingthe particle as it arrives at the bifurcation. In order to sort theparticles, the order at which the particles arrive at the bifurcation,as detected by the self-included unit, is matched to the order of thereceived signal from the decision making unit (e.g. a classifier). Insome aspects, controlled particles are used to align and update theorder as necessary. In some aspects, said controlled particles arespecial calibration beads. In some embodiments the calibration beadsused are polystyrene beads with size ranging between about 1 μM to about50 μM. In some embodiments the calibration beads used are polystyrenebeads with size of least about 1 μM. In some embodiments the calibrationbeads used are polystyrene beads with size of at most about 50 μM. Insome embodiments the calibration beads used are polystyrene beads withsize ranging between about 1 μM to about 3 μM, about 1 μM to about 5 μM,about 1 μM to about 6 μM, about 1 μM to about 10 μM, about 1 μM to about15 μM, about 1 μM to about 20 μM, about 1 μM to about 25 μM, about 1 μMto about 30 μM, about 1 μM to about 35 μM, about 1 μM to about 40 μM,about 1 μM to about 50 μM, about 3 μM to about 5 μM, about 3 μM to about6 μM, about 3 μM to about 10 μM, about 3 μM to about 15 μM, about 3 μMto about 20 μM, about 3 μM to about 25 μM, about 3 μM to about 30 μM,about 3 μM to about 35 μM, about 3 μM to about 40 μM, about 3 μM toabout 50 μM, about 5 μM to about 6 μM, about 5 μM to about 10 μM, about5 μM to about 15 μM, about 5 μM to about 20 μM, about 5 μM to about 25μM, about 5 μM to about 30 μM, about 5 μM to about 35 μM, about 5 μM toabout 40 μM, about 5 μM to about 50 μM, about 6 μM to about 10 μM, about6 μM to about 15 μM, about 6 μM to about 20 μM, about 6 μM to about 25μM, about 6 μM to about 30 μM, about 6 μM to about 35 μM, about 6 μM toabout 40 μM, about 6 μM to about 50 μM, about 10 μM to about 15 μM,about 10 μM to about 20 μM, about 10 μM to about 25 μM, about 10 μM toabout 30 μM, about 10 μM to about 35 μM, about 10 μM to about 40 μM,about 10 μM to about 50 μM, about 15 μM to about 20 μM, about 15 μM toabout 25 μM, about 15 μM to about 30 μM, about 15 μM to about 35 μM,about 15 μM to about 40 μM, about 15 μM to about 50 μM, about 20 μM toabout 25 μM, about 20 μM to about 30 μM, about 20 μM to about 35 μM,about 20 μM to about 40 μM, about 20 μM to about 50 μM, about 25 μM toabout 30 μM, about 25 μM to about 35 μM, about 25 μM to about 40 μM,about 25 μM to about 50 μM, about 30 μM to about 35 μM, about 30 μM toabout 40 μM, about 30 μM to about 50 μM, about 35 μM to about 40 μM,about 35 μM to about 50 μM, or about 40 μM to about 50 μM. In someembodiments the calibration beads used are polystyrene beads with sizeof about 1 μM, about 3 μM, about 5 μM, about 6 μM, about 10 μM, about 15μM, about 20 μM, about 25 μM, about 30 μM, about 35 μM, about 40 μM, orabout 50 μM.

In some embodiments, the sorters used in the systems and methodsdisclosed herein are self-learning cell sorting systems or intelligentcell sorting systems. These sorting systems can continuously learn basedon the outcome of sorting. For example, a sample of cells is sorted, thesorted cells are analyzed, and the results of this analysis are fed backto the classifier.

In some aspects, the sorters used in the systems and methods disclosedherein do not rely on “supervised labels.” In some aspects, the sorterscan identify “different looking” sub-populations in a sample, whereinsaid system automatically identifies and clusters different cell types,in the way of unsupervised learning or semi-supervised learning, byrunning and collecting their features, thus, by imaging them. In someaspects, said system works by running the sample for a few seconds,wherein the classifier then “learns” the components of the sample, thenthe actual sorting of those classes begins. In some aspects, said systemis a sorter system where the sorting decision is made by artificialintelligence (AI), such as deep learning.

In some embodiments, the system and methods of the present disclosurecomprise a cascaded sorting system, wherein a first step bulk sorting iscarried out, wherein a subgroup of particles in a sample are monitoredsimultaneously for a positive particle (for example for a cell with oneor more desired characteristics) and if any positive particle isdetected in the subgroup of particles then, the subgroup of particles issubjected to further sorting. In some embodiments, the cascade sortingcomprises capturing an image of a subgroup of a plurality of cells assaid plurality of cells pass through a first flow channel; and analyzingsaid image for a feature. Following which, if said feature is detectedthen one or more cells in said subgroup are imaged again. In someembodiments, the first step comprises imaging 10 to 100 cells at a timeand if any positive cell is detected, the group of 10 to 100 cells issent to one side of the bifurcation and analyzed again. In some aspects,the first step comprises imaging 20 to 100 cells at a time. In someaspects, the first step comprises imaging 30 to 100 cells at a time. Insome aspects, the first step comprises imaging 40 to 100 cells at atime. In some aspects, the first step comprises imaging 50 to 100 cellsmonitored at a time. In some aspects, the first step comprises imaging50 to 100 cells at a time. In some aspects, the first step comprisesimaging 60 to 100 cells at a time. In some aspects, the first stepcomprises imaging 70 to 100 cells at a time. In some aspects, the firststep comprises imaging 80 to 100 cells at a time. In some aspects, thefirst step comprises imaging 90 to 100 cells at a time. In some aspects,the first step is followed by a next phase sorting of the subgroup ofcells according to the methods described herein. In some embodiments,said next phase sorting is performed by directing the subgroup of cellsto a second flow channel and by imaging the cells in the subgroup,single cell at a time according to the methods described herein.

Sorting Techniques

In some embodiments, the methods and systems disclosed herein can useany sorting technique to sort particles. An exemplary design for sortingthe cells is shown in FIG. 11, wherein desired cell are directedto/sorted into the positive collection reservoir. In some embodiments,the sorting technique comprises closing a channel on one side of thebifurcation to collect the desired cell on the other side. In someaspects, the closing of the channels can be carried out by employing anyknown technique. In some aspects, said closing is carried out byapplication of a pressure. In some instances, said pressure is pneumaticactuation. In some aspects, said pressure can be positive pressure ornegative pressure. In some embodiments, positive pressure is used. Insome examples, one side of the bifurcation is closed by applyingpressure and deflecting the soft membrane between top and bottom layers.Pneumatic actuation is further described in WO2001001025A2, which isincorporated herein by reference in its entirety.

In some aspects, said particles to be sorted are directed to one side ofthe bifurcation by increasing the fluidic resistance on the other side.For example, if one side of the bifurcation can be designed to have alonger channel compared to other side. In such embodiments, the cellsnormally go to the other (shorter) channel even when both valves areopen. In such cases, the cells will go to the longer channel only whenthe valve on the shorter channel is closed.

In some embodiments, said closing of a channel on one side of thebifurcation is carried out by application of an electric filed. In someexamples, said closing of a channel on one side of the bifurcation isachieved by using piezo actuator. For example, a piezo actuator can beused along to the channel. When a voltage is added to the piezo, thepiezo deforms by elongating and/or pinching the channel cross section. Aschematic representation of the use of piezo actuator is shown in FIG.12. In some instances, a deformable material is used for themicrofluidic channels, for example polydimethylsiloxane (PDMS) is used.

In some aspects, said closing of a channel on one side of thebifurcation is carried out by application of a magnetic field. In someexamples, said closing of a channel on one side of the bifurcation isachieved by using magnetic actuation. For example, an electromagnet (EM)can be used along one side of the channel and a magnet can be used onthe other side of the channel. In some instances, when the electromagnetis activated, said magnet becomes attracted to the electromagnet,leaving the channel cross-section to be pinched and closed. In someinstances, a deformable material, for example is used forpolydimethylsiloxane (PDMS) for the microfluidic channels shown in (FIG.13).

Sample and Data Collection

In various embodiments, the systems and methods of the presentdisclosure comprise one or more reservoirs designed to collect theparticles after the particles have been sorted. In some embodiments, thenumber of cells to be sorted is about 1 cell to about 1,000,000 cells.In some embodiments, the number of cells to be sorted is at least about1 cell. In some embodiments, the number of cells to be sorted is at mostabout 1,000,000 cells. In some embodiments, the number of cells to besorted is about 1 cell to about 100 cells, about 1 cell to about 500cells, about 1 cell to about 1,000 cells, about 1 cell to about 5,000cells, about 1 cell to about 10,000 cells, about 1 cell to about 50,000cells, about 1 cell to about 100,000 cells, about 1 cell to about500,000 cells, about 1 cell to about 1,000,000 cells, about 100 cells toabout 500 cells, about 100 cells to about 1,000 cells, about 100 cellsto about 5,000 cells, about 100 cells to about 10,000 cells, about 100cells to about 50,000 cells, about 100 cells to about 100,000 cells,about 100 cells to about 500,000 cells, about 100 cells to about1,000,000 cells, about 500 cells to about 1,000 cells, about 500 cellsto about 5,000 cells, about 500 cells to about 10,000 cells, about 500cells to about 50,000 cells, about 500 cells to about 100,000 cells,about 500 cells to about 500,000 cells, about 500 cells to about1,000,000 cells, about 1,000 cells to about 5,000 cells, about 1,000cells to about 10,000 cells, about 1,000 cells to about 50,000 cells,about 1,000 cells to about 100,000 cells, about 1,000 cells to about500,000 cells, about 1,000 cells to about 1,000,000 cells, about 5,000cells to about 10,000 cells, about 5,000 cells to about 50,000 cells,about 5,000 cells to about 100,000 cells, about 5,000 cells to about500,000 cells, about 5,000 cells to about 1,000,000 cells, about 10,000cells to about 50,000 cells, about 10,000 cells to about 100,000 cells,about 10,000 cells to about 500,000 cells, about 10,000 cells to about1,000,000 cells, about 50,000 cells to about 100,000 cells, about 50,000cells to about 500,000 cells, about 50,000 cells to about 1,000,000cells, about 100,000 cells to about 500,000 cells, about 100,000 cellsto about 1,000,000 cells, or about 500,000 cells to about 1,000,000cells. In some embodiments, the number of cells to be sorted is about 1cell, about 100 cells, about 500 cells, about 1,000 cells, about 5,000cells, about 10,000 cells, about 50,000 cells, about 100,000 cells,about 500,000 cells, or about 1,000,000 cells.

In some embodiments, the number of cells to be sorted is 100 to 500cells, 200 to 500 cells, 300 to 500 cells, 350 to 500 cells, 400 to 500cells, or 450 to 500 cells. In some embodiments, said reservoirs may bemilliliter scale reservoirs. In some examples, the one or morereservoirs are pre-filled with a buffer and the sorted cells are storedin the buffer. Using the buffer helps to increase the volume of thecells, which can then be easily handled, for example a pipetted. In someexamples, the buffer is a phosphate buffer, for examplephosphate-buffered saline (PBS).

In some embodiments, the system and methods of the present disclosurecomprise a cell sorting technique wherein pockets of buffer solutioncontaining no negative objects are sent to the positive output channelin order to push rare objects out of the collection reservoir. In someaspects, additional buffer solution is sent to said positive outputchannel to flush out all positive objects at the end of a run, once thechannel is flushed clean.

Real-Time Integration

In some embodiments, the system and methods of the present disclosurecomprise a combination of techniques, wherein a graphics processing unit(GPU) and a digital signal processor (DSP) are used to run artificialintelligence (AI) algorithms and apply classification results inreal-time to the system. In some aspects, the system and methods of thepresent disclosure comprise a hybrid method for real-time cell sorting.

Validation

In some embodiments, the systems disclosed herein further comprise avalidation unit that detects the presence of a particle without gettingdetailed information, such as imaging. In some instances, saidvalidation unit may be used for one or more purposes. For e.g. in someexamples, said validation unit detects a particle approaching thebifurcation and enables precise sorting. In some examples, saidvalidation unit provides timing information with two laser spots. Insome instances, said validation unit provides timing information byreferencing the imaging time. In some instances, said validation unitprovides precise time delay information and/or flow speed of particles.

In some embodiments, said validation unit is a laser-assisted system. Insome aspects, said laser-assisted system measures the laser blockage orscattering, and thereby the size of the particle may be inferred. Insome aspects, said laser-assisted system measures the laser blockagescattering, and thereby other physical information of the particle maybe inferred, including but not limited to the size, the shape, thedensity, and the texture. In some aspects, said laser-assisted systemmeasures the laser blockage or scattering, and thereby the transientspeed of the object may be inferred.

In some embodiments, the validation unit, for e.g. the laser-assistedsystem, is located after the bifurcation (for e.g. on the positive side,on the negative side, or on both sides). In some examples, such alocation of the validation unit provides clarification information onthe accuracy and effectiveness of the sorting. In some aspects, thevalidation system may be used to create a closed feedback loop to adjustthe control parameters of the system. In some instances, said closedfeedback loop can be used to improve system accuracy and/or toaccommodate slow system draft over the course of the experimental run.

In some embodiments, the validation unit comprises a laser-assistedsystem wherein two or more laser spots are utilized. When the two ormore laser spots are closely placed, different methods, disclosedherein, are used to create said the two or more laser spots and laterseparate into two or more independent signals for photo detection. Insome embodiments, the distance between the two or more closely spacedlaser spots is in the range of 10 μm-about 1,000 μm, for example saiddistance is between 10 μm to 500 μm, between 50 μm to 500 μm, 100 μm to500 μm, 150 μm to 500 μm, between 200 μm to 500 μm, between 250 μm to500 μm, between 300 μm to 500 μm, between 350 μm to 500 μm, between 400μm to 500 μm, or between 450 μm to 500 μm.

In some embodiments, said two or more closely spaced laser spots comefrom different optical trains with their respective tilt angle. In someaspects, said two or more closely spaced lasers pass through the samefocusing optics but come in with slightly different angles and thus, getfocused with slight offset to each other. In some aspects, said two ormore closely spaced laser spots have different wavelengths and arerecombined with slight offset through a dichroic mirror that passes somelasers while reflecting the other lasers, wherein with the properarrangement, said lasers can come out together with the desired offset.In some aspects, said two or more lasers spots have differentpolarizations and can be recombined with a desired offset through apolarization beam splitter. In some aspects, said two or more closelyspaced lasers are generated by recombining with a close proximitythrough a general purpose beam-splitter as shown in FIG. 14.

In some embodiments, the signals from the two or more closely spacedlasers can be separated by reversing the creation methods describedherein. In some aspects, the signals of the two or more lasers can beseparated using optical components with a good alignment. In someinstances, said optical component is a round glass ball. In someaspects, the signals of the two or more lasers can be separated bysending the signal to a position-sensitive detector. In some instances,said position-sensitive detector is a dual photodetector. In someinstances, said position-sensitive detector is a quad photodetector. Insome aspects, the present disclosure comprises a laser-assisted systemwherein two closely spaced laser spots are utilized. In some aspects,said laser spots comprise a slit in between, wherein the change indifferent detection area is used to calculate the original two lasersignal changes.

In some embodiments, the validation of particle sorting by the methodsand the system disclosed herein is performed by the using the backchannel design as shown in FIG. 15. In these embodiments, the validationcomprises imaging both the flow channel where the cells are imaged forthe first time, known as the forward channel, and the outlet, where thecells flow after being sorted. In some aspects, the outlet is designedas a backchannel that fits in the same field of view as the imagingdevice used to image the particles in the flow channel. In some aspects,the outlet particles can be identified against the forward channelparticles by their location and/or velocity. In some instances, thevelocity of the particles is positive in the forward channel. In someinstances, the velocity of the particles is negative in the backwardchannel.

Samples

In some embodiments, the particles (for e.g. cells) analyzed by thesystems and methods disclosed herein are comprised in a sample. Thesample may be a biological sample obtained from a subject. In someembodiments, the biological sample comprises a biopsy sample from asubject. In some embodiments, the biological sample comprises a tissuesample from a subject. In some embodiments, the biological samplecomprises liquid biopsy from a subject. In some embodiments, thebiological sample can be a solid biological sample, e.g., a tumorsample. In some embodiments, a sample from a subject can comprise atleast about 1%, at least about 5%, at least about 10%, at least about15%, at least about 20%, at least about 25%, at least about 30%, atleast about 35%, at least about 40%, at least about 45%, at least about50%, at least about 55%, at least about 60%, at least about 65%, atleast about 70%, at least about 75%, at least about 80%, at least about85%, at least about 90%, at least about 95%, at least about 96%, atleast about 97%, at least about 98%, at least about 99%, or at leastabout 100% tumor cells from a tumor.

In some embodiments, the sample can be a liquid biological sample. Insome embodiments, the liquid biological sample can be a blood sample(e.g., whole blood, plasma, or serum). A whole blood sample can besubjected to separation of cellular components (e.g., plasma, serum) andcellular components by use of a Ficoll reagent. In some embodiments, theliquid biological sample can be a urine sample. In some embodiments, theliquid biological sample can be a perilymph sample. In some embodiments,the liquid biological sample can be a fecal sample. In some embodiments,the liquid biological sample can be saliva. In some embodiments, theliquid biological sample can be semen. In some embodiments, the liquidbiological sample can be amniotic fluid. In some embodiments, the liquidbiological sample can be cerebrospinal fluid. In some embodiments, theliquid biological sample can be bile. In some embodiments, the liquidbiological sample can be sweat. In some embodiments, the liquidbiological sample can be tears. In some embodiments, the liquidbiological sample can be sputum. In some embodiments, the liquidbiological sample can be synovial fluid. In some embodiments, the liquidbiological sample can be vomit.

In some embodiments, samples can be collected over a period of time andthe samples may be compared to each other or with a standard sampleusing the systems and methods disclosed herein. In some embodiments thestandard sample is a comparable sample obtained from a differentsubject, for example a different subject that is known to be healthy ora different subject that is known to be unhealthy. Samples can becollected over regular time intervals, or can be collectedintermittently over irregular time intervals.

In some embodiments, the subject may be an animal (e.g., human, rat,pig, horse, cow, dog, mouse). In some instances, the subject is a humanand the sample is a human sample. The sample may be a fetal humansample. The sample may be from a multicellular tissue (e.g., an organ(e.g., brain, liver, lung, kidney, prostate, ovary, spleen, lymph node,thyroid, pancreas, heart, skeletal muscle, intestine, larynx, esophagus,and stomach), a blastocyst). The sample may be a cell from a cellculture. In some sample the subject is a pregnant human, or a humansuspected to be pregnant.

The sample may comprise a plurality of cells. The sample may comprise aplurality of the same type of cell. The sample may comprise a pluralityof different types of cells. The sample may comprise a plurality ofcells at the same point in the cell cycle and/or differentiationpathway. The sample may comprise a plurality of cells at differentpoints in the cell cycle and/or differentiation pathway.

The plurality of samples may comprise one or more malignant cell. Theone or more malignant cells may be derived from a tumor, sarcoma, orleukemia.

The plurality of samples may comprise at least one bodily fluid. Thebodily fluid may comprise blood, urine, lymphatic fluid, saliva. Theplurality of samples may comprise at least one blood sample.

The plurality of samples may comprise at least one cell from one or morebiological tissues. The one or more biological tissues may be a bone,heart, thymus, artery, blood vessel, lung, muscle, stomach, intestine,liver, pancreas, spleen, kidney, gall bladder, thyroid gland, adrenalgland, mammary gland, ovary, prostate gland, testicle, skin, adipose,eye or brain.

The biological tissue may comprise an infected tissue, diseased tissue,malignant tissue, calcified tissue or healthy tissue.

Non-Invasive Prenatal Testing (NIPT)

Conventional prenatal screening methods for detecting fetalabnormalities and for sex determination use fetal samples acquiredthrough invasive techniques, such as amniocentesis and chorionic villussampling (CVS). Ultrasound imaging is also used to detect structuralmalformations such as those involving the neural tube, heart, kidney,limbs and the like. Chromosomal aberrations such as the presence ofextra chromosomes, such as Trisomy 21 (Down syndrome), Klinefelter'ssyndrome, Trisomy 13 (Patau syndrome), Trisomy 18 (Edwards syndrome), orthe absence of chromosomes, such as Turner's syndrome, or varioustranslocations and deletions can be currently detected using CVS and/oramniocentesis. Both techniques require careful handling and present adegree of risk to the mother and to the pregnancy.

Prenatal diagnosis is offered to women over the age of 35 and/or womenwho are known to carry genetic diseases, as balanced translocations ormicrodeletions.

Chorionic villus sampling (CVS) is performed between the 9^(th) and the14^(th) week of gestation. CVS involves the insertion of a catheterthrough the cervix or the insertion of a needle into the abdomen of thesubject/patient. The needle or catheter is used to remove a small sampleof the placenta, known as the chorionic villus. The fetal karyotype isthen determined within one to two weeks of the CVS procedure. Due to theinvasive nature of the CVS procedure, there is a 2 to 4%procedure-related risk of miscarriage. CVS is also associated with anincreased risk of fetal abnormalities, such as defective limbdevelopment, which are presumably due to hemorrhage or embolism from theaspirated placental tissues.

Amniocentesis is performed between the 16^(th) and the 20^(th) week ofgestation. Amniocentesis involves the insertion of a thin needle throughthe abdomen into the uterus of the patient. This procedure carries a 0.5to 1% procedure-related risk of miscarriage. Amniotic fluid is aspiratedby the needle and fetal fibroblast cells are further cultured for 1 to 2weeks, following which they are subjected to cytogenetic and/orfluorescence in situ hybridization (FISH) analyses.

Recent techniques have been developed to predict fetal abnormalities andpredict possible complications in pregnancy. These techniques usematerial blood or serum samples and have focused on the use of threespecific markers, including alpha-fetoprotein (AFP), human chorionicgonadotrophin (hCG), and estriol. These three markers are used to screenfor Down's syndrome and neural tube defects. Maternal serum is currentlybeing used for biochemical screening for chromosomal aneuploidies andneural tube defects.

The passage of nucleated cells between the mother and fetus is awell-studied phenomenon. Using the fetal cells that are present inmaternal blood for non-invasive prenatal diagnosis prevents the risksthat are usually associated with conventional invasive techniques. Fetalcells include fetal trophoblasts, leukocytes, and nucleated erythrocytesfrom the maternal blood during the first trimester of pregnancy. Thissaid, the isolation of trophoblasts from the maternal blood is limitedby their multinucleated morphology and the availability of antibodies,whereas the isolation of leukocytes is limited by the lack of uniquecell markers which differentiate maternal from fetal leukocytes.Furthermore, since leukocytes may persist in the maternal blood for aslong as 27 years, residual cells are likely to be present in thematernal blood from previous pregnancies.

In some embodiments, the system and methods disclosed herein are usedfor non-invasive prenatal testing (NIPT), wherein said methods are usedto analyze maternal serum or plasma samples from a pregnant female. Insome aspects, said system and methods are used for non-invasive prenataldiagnosis. In some aspects, the system and methods disclosed herein canbe used to analyze maternal serum or plasma samples derived frommaternal blood. In some aspects, as little as 10 μL of serum or plasmacan be used. In some aspects, larger samples are used to increaseaccuracy, wherein the volume of the sample used is dependent upon thecondition or characteristic being detected.

In some embodiments, the system and methods disclosed herein are usedfor non-invasive prenatal diagnosis including but not limited to sexdetermination, blood typing and other genotyping, detection ofpre-eclampsia in the mother, determination of any maternal or fetalcondition or characteristic related to either the fetal DNA itself orthe quantity or quality of the fetal DNA in the maternal serum orplasma, and identification of major or minor fetal malformations orgenetic diseases present in a fetus. In some aspects, fetus is a humanfetus.

In some embodiments, the system and methods disclosed herein are used toanalyze serum or plasma from maternal blood samples, wherein said serumor plasma preparation is carried out by standard techniques andsubjected to a nucleic acid extraction process. In some aspects, saidserum or plasma is extracted using a proteinase K treatment followed byphenol/chloroform extraction.

In some embodiments, the system and methods disclosed herein are used toimage cells from maternal serum or plasma acquired from a pregnantfemale subject. In some aspects, said subject is a human. In someaspects, said pregnant female human subject is over the age of 35. Insome aspects, said pregnant female human subject is known to carry agenetic disease. In some aspects, said subject is a human. In someaspects, said pregnant female human subject is over the age of 35 and isknown to carry a genetic disease.

In some embodiments, the system and methods disclosed herein are used toanalyze fetal cells from maternal serum or plasma. In some aspects, thecells that are used for non-invasive prenatal testing using the systemand methods disclosed herein are fetal cells such as fetal trophoblasts,leukocytes, and nucleated erythrocytes. In some aspects, fetal cells arefrom the maternal blood during the first trimester of pregnancy.

In some embodiments, the system and methods disclosed herein are usedfor non-invasive prenatal diagnosis using fetal cells comprisingtrophoblast cells. In some aspects, trophoblast cells using the presentdisclosure are retrieved from the cervical canal using aspiration. Insome aspects, trophoblast cells using the present disclosure areretrieved from the cervical canal using cytobrush or cotton wool swabs.In some aspects, trophoblast cells using the present disclosure areretrieved from the cervical canal using endocervical lavage. In someaspects, trophoblast cells using the present disclosure are retrievedfrom the cervical canal using intrauterine lavage.

In some embodiments, the system and methods disclosed herein are used toanalyze fetal cells from maternal serum or plasma, wherein the cellpopulation is mixed and comprises fetal cells and maternal cells. Insome aspects, the system and methods of the present disclosure are usedto identify embryonic or fetal cells in a mixed cell population. In someembodiments, the system and methods of the present disclosure are usedto identify embryonic or fetal cells in a mixed cell population, whereinnuclear size and shape are used to identify embryonic or fetal cells ina mixed population. In some embodiments, the systems and methodsdisclosed herein are used to sort fetal cells from a cell population.

In some embodiments, the system and methods disclosed herein are used tomeasure the count of fetal nucleated red blood cells (RBCs), wherein anincrease in fetal nucleated RBC count indicates the presence of fetalaneuploidy.

In some embodiments, the system and methods disclosed herein are used toimage cells from maternal serum or plasma acquired from a pregnantfemale subject. In some aspects, said cells are not labelled. In someaspects, said cells are in a flow. In some aspects, said cells areimaged from different angles. In some aspects, said cells are livecells. In some aspects, said cells are housed in a flow channel withinthe system of the present disclosure, wherein the flow channel has wallsformed to space the plurality of cells within a single streamline. Insome aspects, said cells are housed in a flow channel within the systemof the present disclosure, wherein the flow channel has walls formed torotate the plurality of said cells within a single streamline.

In some embodiments, the system and methods disclosed herein are used toimage cells from maternal serum or plasma acquired from a pregnantfemale subject. In some aspects, a plurality of images of said cells iscollected using the system and methods of the present disclosure. Insome aspects, the plurality of images is analyzed to determine ifspecific disease conditions are present in the subject, wherein saidcells are in a flow during the imaging and wherein the plurality ofimages comprises images of said cells from a plurality of angles. Insome aspects, subject is the fetus. In some aspects, subject is pregnantfemale subject.

Sperm Analysis

In some embodiments, the sample used in the methods and systemsdescribed herein is a semen sample, and the system and methods of thepresent disclosure are used to identify sperm quality and/or gender. Inthese embodiments, the methods described herein comprise imaging thesemen sample from the subject according to the methods described hereinand analyzing the sperms in the semen sample for one or more features.In some embodiments, the systems and methods described herein are usedto obtain a sperm count. In some aspects, the systems and methodsdescribed herein are used to obtain information about sperm viabilityand/or health. In some aspects, the systems and methods described hereinare used to obtain information about sperm gender. In some embodiments,the sorting systems and methods described herein are used for andautomated enrichment of sperms with desired morphological features. Insome embodiment, the enriched sperms obtained according to the methodsand systems described herein are used for in-vitro fertilization. Insome aspects, said features are associated with health, motility, and/orgender.

Cancer Cells

Many cancers are diagnosed in later stages of the disease because of lowsensitivity of existing diagnostic procedures and processes. More than1.5 million people are diagnosed with cancer every year in the USA, ofwhich 600,000 people die (Jemal et al. 2010). Currently, the firstcancer screening procedure involves the detection of a tumor. Manycancer tumors, such as breast cancer are detected by self- or clinicalexamination. However, these tumors are typically detected only after thetumor reach a volume of 1 mL or 1 cc, when it contains approximately 10⁹cells. Routine screening by mammography is more sensitive and allowsdetection of a tumor before it becomes palpable, but only after theyreach an inch in diameter. MRI, positron emission tomography (PET) andsingle-photon emission computed tomography (SPECT) can reveal evensmaller tumors than can be detected by mammograms. However, theseimaging methods present significant disadvantages. Contrast agents formagnetic resonance imaging (MRI) are toxic and radionuclides deliveredfor SPECT or PET examination are sources of ionizing radiation. Becauseof its relatively poor resolution, ovarian cancer often requires severalfollow up scans with computed tomography (CT) or MRI, while undertakingall precautions to protect possible pregnancies, to reveal fine anatomyof developing tumors (Shin et al. 2011). Additionally, all of thesediagnostic techniques require dedicated facilities, expensive equipment,well trained staff, and financial coverages.

Cancer is commonly diagnosed in patients by obtaining a sample of thesuspect tissue and examining said tissue under a microscope for thepresence of malignant cells. While this process is relativelystraightforward when the anatomic location of the suspect tissue isknown, it can become quite challenging when there is no readilyidentifiable tumor or pre-cancerous lesion. For example, to detect thepresence of lung cancer from a sputum sample requires one or morerelatively rare cancer cells to be present in the sample. Therefore,patients having lung cancer may not be diagnosed properly if the sampledoes not perceptively and accurately reflect the conditions of the lung.

Conventional light microscopy, which utilizes cells mounted on glassslides, can only approximate 2D and 3D measurements because oflimitations in focal plane depth, sampling angles, and problems withcell preparations that typically cause cells to overlap in the plane ofthe image. Another drawback of light microscopy is the inherentlimitation of viewing through an objective lens where only the areawithin the narrow focal plane provides accurate data for analysis.

Flow cytometry methods generally overcome the cell overlap problem bycausing cells to flow one-by-one in a fluid stream. Unfortunately, flowcytometry systems do not generate images of cells of the same quality astraditional light microscopy, and, in any case, the images are notthree-dimensional.

In some embodiments, the system and methods disclosed herein enable theacquisition of three-dimensional imaging data of individual cells,wherein each individual cell from a cell population is imaged from aplurality of angles. In some aspects, the present disclosure is used todiagnose cancer, wherein individual cancer cells are identified,tracked, and grouped together. In some aspects, said cells are live.

In some embodiments, the system and methods disclosed herein are usedfor cancer diagnosis in a subject, the method comprising imaging a cellin a biological sample from the subject to collect a plurality of imagesof the cell and analyzing the plurality of images to determine ifcancerous cells are present in the subject, wherein the cancerous cellis in a flow during imaging and is spinning, and wherein the pluralityof images comprise images from a different spinning angles.

In some embodiments, the system and methods disclosed herein are usedfor cancer cell detection, wherein the cancerous cells are frombiological samples and are detected and tracked as they pass through thesystem of the present disclosure.

In some embodiments, the system and methods disclosed herein are used toidentify cancer cells from biological samples acquired from mammaliansubjects, wherein the cell population is analyzed by nuclear detail,nuclear contour, presence or absence of nucleoli, quality of cytoplasm,quantity of cytoplasm, nuclear aspect ratio, cytoplasmic aspect ratio,or nuclear to cytoplasmic ratio. In some aspects, the cancer cells thatare identified indicate the presence of cancer in the mammalian sample,including but not limited to, lymphoma, myeloma, neuroblastoma, breastcancer, ovarian cancer, lung cancer, rhabdomyosarcoma, small-cell lungtumors, primary brain tumors, stomach cancer, colon cancer, pancreaticcancer, urinary bladder cancer, testicular cancer, lymphomas, thyroidcancer, neuroblastoma, esophageal cancer, genitourinary tract cancer,cervical cancer, endometrial cancer, adrenal cortical cancer, orprostate cancer. In some aspects, the said cancer is metastatic cancer.In some aspects, the said cancer is an early stage cancer.

In some embodiments, the system and methods disclosed herein are used toimage a large number of cells from a subject and collect a plurality ofimages of the cell, and to then classify the cells based on an analysisof one or more of the plurality of images; wherein the plurality ofimages comprise images from a plurality of cell angles and wherein thecell is tracked until the cell has been classified. In some aspects, thetracked cells are classified as cancerous. In some aspects, the subjectis a human.

In some embodiments, the cells used in the methods disclosed herein arelive cells. In some aspects, the cells that are classified as cancerouscells are isolated and subsequently cultured for potential drug compoundscreening, testing of a biologically active molecule, and/or furtherstudies.

In some embodiments, the system and methods disclosed herein are used toidentify cancer cells from a cell population from a mammalian subject.In some aspects, said subject is a human. In some aspects, the systemand methods disclosed herein are used to determine the progression of acancer, wherein samples from a subject are obtained from two differenttime points and compared using the methods of the present disclosure. Insome aspects, the system and methods disclosed herein are used todetermine the effectiveness of an anti-cancer treatment, wherein samplesfrom a subject are obtained before and after anti-cancer treatment andcomparing the two samples using the methods of the present disclosure.

In some embodiments, the system and methods disclosed herein comprise acancer detection system that uses a rapidly trained neural network,wherein said neural network detects cancerous cells by analyzing rawimages of the cell and provides imaging information from the pixels ofthe images to a neural network. In some aspects, said neural networkperforms recognition and identification of cancerous cells usinginformation derived from an image of the cells, among others, the area,the average intensity, the shape, the texture, and the DNA (pgDNA) ofthe cells. In some aspects, said neural network performs recognition ofcancerous cells using textural information derived from an image of thecells, among them angular second moment, contrast, coefficient ofcorrelation, sum of squares, difference moment, inverse differencemoment, sum average, sum variance, sum entropy, entry, differencevariance, difference entropy, information measures, maximal correlationcoefficient, coefficient of variation, peak transition probability,diagonal variance, diagonal moment, second diagonal moment, productmoment, triangular symmetry and blobness.

Bacteria from Human Cells

In some embodiments, the methods disclosed herein are used for bacterialdetection, wherein the human cells containing bacteria are frombiological samples and are detected and tracked as they pass through thesystem of the present disclosure.

In some embodiments, the system and methods disclosed herein enable theacquisition of three-dimensional imaging data of bacteria present in asample, wherein each individual bacterium is imaged from a plurality ofangles. In some embodiments, the system and methods disclosed herein areused for bacterial detection, wherein the bacteria is from biologicalsamples and are detected and tracked as they pass through the system ofthe present disclosure.

In some embodiments, the system and methods disclosed herein are used todetect bacteria in fluids, including blood, platelets, and other bloodproducts for transfusion, and urine. In some aspects, the presentdisclosure provides a method for separating intact eukaryotic cells fromsuspected intact bacterial cells that may be present in the fluidsample. In some aspects, the present disclosure identifies certainbacterial species, including but not limited to: Bacillus cereus,Bacillus subtilis, Clostridium perfringens, Corynebacterium species,Escherichia coli, Enterobacter cloacae, Klebsiella oxytoca,Propionibacterium acnes, Pseudomonas aeruginosa, Salmonellacholeraesuis, Serratia marcesens, Staphylococcus aureus, Staphylococcusepidermidis, Streptococcus pyogenes, and Streptococcus viridans.

In some embodiments, the system and methods disclosed herein comprise abacterial detection system that uses a rapidly trained neural network,wherein said neural network detects bacteria by analyzing raw images ofthe cell and provides imaging information from the pixels of the imagesto a neural network. In some aspects, said neural network performsrecognition and identification of bacteria using information derivedfrom an image of the bacteria, among others, the area, the averageintensity, the shape, the texture, and the DNA (pgDNA) of the cells. Insome aspects, said neural network performs recognition of cancerouscells using textural information derived from an image of the cells,among them angular second moment, contrast, coefficient of correlation,sum of squares, difference moment, inverse difference moment, sumaverage, sum variance, sum entropy, entry, difference variance,difference entropy, information measures, maximal correlationcoefficient, coefficient of variation, peak transition probability,diagonal variance, diagonal moment, second diagonal moment, productmoment, triangular symmetry and blobness.

Sickle Cell Disease

In some embodiments, the system and methods disclosed herein are usedfor the detection and/or identification of a sickle cell. In someaspects, the system and methods disclosed herein are used to image acell and to determine if the cell is a sickle cell. The methods of thedisclosure may be further used to collect the cells determined to besickle cells. In some embodiments the cell is from a biological samplefrom a subject and the methods disclosed herein are used to determinewhether the subject suffers from or is susceptible to a sickle celldisease. In some embodiments, the sickle cell disease is a sickle cellanemia.

Crystals in Biological Samples

Current diagnostic methods used to detect crystals in blood and/or urineincludes radiological, serological, sonographic, and enzymatic methods.

Urine crystals may be of several different types. Most commonly crystalsare formed of struvite (magnesium-ammonium-phosphate), oxalate, urate,cysteine, or silicate, but may also be composed of other materials suchas bilirubin, calcium carbonate, or calcium phosphate.

In some embodiments, the system and methods disclosed herein are usedfor the detection of crystals in biological samples. In some aspects,detected crystals are formed. In some aspects, said biological samplefrom a subject is imaged according to the methods described herein todetermine whether the biological sample comprises a crystal. In someaspects, said biological sample is blood. In some aspects, said blood isvenous blood of a subject. In some aspects, said biological sample isurine. In some aspects, said subject is a human, horse, rabbit, guineapig, or goat. In some aspects, the methods of the disclosure may befurther utilized to isolate and collect the crystal from the sample. Insome aspects, said biological sample is from a subject and the systemand methods of the present disclosure are used to determine whether thesubject suffers from or is susceptible to disease or a condition.

In some embodiments, the methods disclosed herein are used for theanalysis of a crystal from a biological sample. In some aspects, themethods disclosed herein may be used to image a crystal, and the crystalimages may be analyzed for, including but not limited to, crystal shape,size, texture, morphology, and color. In some embodiments, thebiological sample is from a subject and the methods disclosed herein areused to determine whether the subject suffers from a disease or acondition. In some example the subject is a human. For example, themethods of the disclosure may be used to analyze crystal in a bloodsample of the human subject, and the results may be used to determinewhether the subject suffers from pathological conditions, including butnot limited to, chronic or rheumatic leukemia. In some aspects, saidbiological sample is a urine sample.

In some embodiments, the system and methods disclosed herein enable theacquisition of three-dimensional imaging data of crystals, if found inthe biological sample, wherein each individual crystal is imaged from aplurality of angles.

In some embodiments, the system and methods disclosed herein comprise acrystal detection system that uses a rapidly trained neural network,wherein said neural network detects crystals by analyzing raw images ofa plurality of crystals and provides imaging information from the pixelsof the images to a neural network. In some aspects, said neural networkperforms recognition and identification of a plurality of crystals usinginformation derived from an image of the crystals, among others, thearea, the average intensity, the shape, the texture. In some aspects,said neural network performs recognition of crystals using texturalinformation derived from an image of the cells, among them angularsecond moment, contrast, coefficient of correlation, sum of squares,difference moment, inverse difference moment, sum average, sum variance,sum entropy, entry, difference variance, difference entropy, informationmeasures, maximal correlation coefficient, coefficient of variation,peak transition probability, diagonal variance, diagonal moment, seconddiagonal moment, product moment, triangular symmetry and blobness.

Liquid Biopsy

A liquid biopsy comprises the collection of blood and/or urine from acancer patient with primary or recurrent disease and the analysis ofcancer-associated biomarkers in the blood and/or urine. A liquid biopsyis a simple and non-invasive alternative to surgical biopsies thatenables doctors to discover a range of information about a tumor. Liquidbiopsies are increasingly being recognized as a viable, noninvasivemethod of monitoring a patient's disease progression, regression,recurrence, and/or response to treatment.

In some embodiments, the methods disclosed herein are used for liquidbiopsy diagnostics, wherein the biopsy is a liquid biological samplethat is passed through the system of the present disclosure. In someaspects, the liquid biological sample that is used for the liquid biopsyis less than 5 mL of liquid. In some aspects, the liquid biologicalsample that is used for the liquid biopsy is less than 4 mL of liquid.In some aspects, the liquid biological sample that is used for theliquid biopsy is less than 3 mL of liquid. In some aspects, the liquidbiological sample that is used for the liquid biopsy is less than 2 mLof liquid. In some aspects, the liquid biological sample that is usedfor the liquid biopsy is less than 1 mL of liquid. In some aspects, theliquid biological sample that is used for liquid biopsy is centrifugedto get plasma.

In some embodiments, the system and methods of the present disclosureare used for body fluid sample assessment, wherein cells within a sampleare imaged and analyzed and a report is generated comprising all thecomponents within the sample, the existence of abnormalities in saidsample, and a comparison to previously imaged or tested samples from thesame patient or the baseline of other healthy individuals.

In some embodiments, the system and methods of the present disclosureare used for the diagnosis of immune diseases, including but not limitedto tuberculosis (TB) and acquired immune deficiency disorder (AIDS),wherein white blood cells are imaged in the system disclosed herein toexamine their capacity to release pro- and anti-inflammatory cytokines.

In some embodiments, the system and methods of the present disclosureare used to assess patient immune responses to immunomodulatorytherapies by imaging their white blood cells and analyzing the change intheir capacity to release pro- and anti-inflammatory cytokines.

In some embodiments, the system and methods of the present disclosureare used to identify the efficacy of therapeutics and/or to guide theselection of agents or their dosage by isolating patients' white bloodcells and analyzing the effect of target therapeutics on their capacityto release pro- and anti-inflammatory cytokines.

In some embodiments, the system and methods of the present disclosureare used to isolate pure samples of stem cell-derived tissue cells byobtaining images of cells, and isolating cells with desired phenotype.

Testing Biologically Active Molecules

In some embodiments, the methods disclosed herein are used forbiologically active molecule testing, for example drugs. In someembodiments, the methods of the disclosure are sued to collect desiredcells from a sample and then treating the desired cells with abiologically active molecule in order to test the effect of thebiologically active molecule on the collected cells.

In some embodiments, the methods and systems of the present disclosureare used for identifying the efficacy of therapeutics. In some aspects,identifying the efficacy of therapeutics using the system disclosedherein is carried out by obtaining images of a cell before and aftertreatment and analyzing the images to determine whether said cell hasresponded to the therapeutic of interest.

In some embodiments, the system and methods disclosed herein are usedfor diseased cell detection, wherein the diseased cells are frombiological samples and are detected and tracked as they pass through thesystem of the present disclosure. In some aspects, said diseased cellsare isolated and grouped together for further studies.

In some embodiments, the cells used in the methods disclosed herein arelive cells. In some aspects, the cells that are classified as diseasedcells are isolated and subsequently cultured for potential drug compoundscreening, testing of a biologically active molecule, and/or furtherstudies.

Although the present disclosure has been described in certain specificaspects, many additional modifications and variations would be apparentto those skilled in the art. It is therefore to be understood that thepresent disclosure can be practiced otherwise than specificallydescribed without departing from the scope and spirit of the presentdisclosure. Thus, some embodiments of the present disclosure should beconsidered in all respects as illustrative and not restrictive.

Computer Systems

The present disclosure provides computer systems that are programmed toimplement methods of the disclosure. FIG. 16 shows a computer system1601 that is programmed or otherwise configured to capture and/oranalyze one or more images of the cell. The computer system 1601 canregulate various aspects of components of the cell sorting system of thepresent disclosure, such as, for example, the pump, the valve, and theimaging device. The computer system 1601 can be an electronic device ofa user or a computer system that is remotely located with respect to theelectronic device. The electronic device can be a mobile electronicdevice.

The computer system 1601 includes a central processing unit (CPU, also“processor” and “computer processor” herein) 1605, which can be a singlecore or multi core processor, or a plurality of processors for parallelprocessing. The computer system 1601 also includes memory or memorylocation 1610 (e.g., random-access memory, read-only memory, flashmemory), electronic storage unit 1615 (e.g., hard disk), communicationinterface 1620 (e.g., network adapter) for communicating with one ormore other systems, and peripheral devices 1625, such as cache, othermemory, data storage and/or electronic display adapters. The memory1610, storage unit 1615, interface 1620 and peripheral devices 1625 arein communication with the CPU 1605 through a communication bus (solidlines), such as a motherboard. The storage unit 1615 can be a datastorage unit (or data repository) for storing data. The computer system1601 can be operatively coupled to a computer network (“network”) 1630with the aid of the communication interface 1620. The network 1630 canbe the Internet, an internet and/or extranet, or an intranet and/orextranet that is in communication with the Internet. The network 1630 insome cases is a telecommunication and/or data network. The network 1630can include one or more computer servers, which can enable distributedcomputing, such as cloud computing. The network 1630, in some cases withthe aid of the computer system 1601, can implement a peer-to-peernetwork, which may enable devices coupled to the computer system 1601 tobehave as a client or a server.

The CPU 1605 can execute a sequence of machine-readable instructions,which can be embodied in a program or software. The instructions may bestored in a memory location, such as the memory 1610. The instructionscan be directed to the CPU 1605, which can subsequently program orotherwise configure the CPU 1605 to implement methods of the presentdisclosure. Examples of operations performed by the CPU 1605 can includefetch, decode, execute, and writeback.

The CPU 1605 can be part of a circuit, such as an integrated circuit.One or more other components of the system 1601 can be included in thecircuit. In some cases, the circuit is an application specificintegrated circuit (ASIC).

The storage unit 1615 can store files, such as drivers, libraries andsaved programs. The storage unit 1615 can store user data, e.g., userpreferences and user programs. The computer system 1601 in some casescan include one or more additional data storage units that are externalto the computer system 1601, such as located on a remote server that isin communication with the computer system 1601 through an intranet orthe Internet.

The computer system 1601 can communicate with one or more remotecomputer systems through the network 1630. For instance, the computersystem 1601 can communicate with a remote computer system of a user.Examples of remote computer systems include personal computers (e.g.,portable PC), slate or tablet PC's (e.g., Apple® iPad, Samsung® GalaxyTab), telephones, Smart phones (e.g., Apple® iPhone, Android-enableddevice, Blackberry®), or personal digital assistants. The user canaccess the computer system 1601 via the network 1630.

Methods as described herein can be implemented by way of machine (e.g.,computer processor) executable code stored on an electronic storagelocation of the computer system 1601, such as, for example, on thememory 1610 or electronic storage unit 1615. The machine executable ormachine readable code can be provided in the form of software. Duringuse, the code can be executed by the processor 1605. In some cases, thecode can be retrieved from the storage unit 1615 and stored on thememory 1610 for ready access by the processor 1605. In some situations,the electronic storage unit 1615 can be precluded, andmachine-executable instructions are stored on memory 1610.

The code can be pre-compiled and configured for use with a machinehaving a processer adapted to execute the code, or can be compiledduring runtime. The code can be supplied in a programming language thatcan be selected to enable the code to execute in a pre-compiled oras-compiled fashion.

Aspects of the systems and methods provided herein, such as the computersystem 1601, can be embodied in programming. Various aspects of thetechnology may be thought of as “products” or “articles of manufacture”typically in the form of machine (or processor) executable code and/orassociated data that is carried on or embodied in a type of machinereadable medium. Machine-executable code can be stored on an electronicstorage unit, such as memory (e.g., read-only memory, random-accessmemory, flash memory) or a hard disk. “Storage” type media can includeany or all of the tangible memory of the computers, processors or thelike, or associated modules thereof, such as various semiconductormemories, tape drives, disk drives and the like, which may providenon-transitory storage at any time for the software programming. All orportions of the software may at times be communicated through theInternet or various other telecommunication networks. Suchcommunications, for example, may enable loading of the software from onecomputer or processor into another, for example, from a managementserver or host computer into the computer platform of an applicationserver. Thus, another type of media that may bear the software elementsincludes optical, electrical and electromagnetic waves, such as usedacross physical interfaces between local devices, through wired andoptical landline networks and over various air-links. The physicalelements that carry such waves, such as wired or wireless links, opticallinks or the like, also may be considered as media bearing the software.As used herein, unless restricted to non-transitory, tangible “storage”media, terms such as computer or machine “readable medium” refer to anymedium that participates in providing instructions to a processor forexecution.

Hence, a machine readable medium, such as computer-executable code, maytake many forms, including but not limited to, a tangible storagemedium, a carrier wave medium or physical transmission medium.Non-volatile storage media include, for example, optical or magneticdisks, such as any of the storage devices in any computer(s) or thelike, such as may be used to implement the databases, etc. shown in thedrawings. Volatile storage media include dynamic memory, such as mainmemory of such a computer platform. Tangible transmission media includecoaxial cables; copper wire and fiber optics, including the wires thatcomprise a bus within a computer system. Carrier-wave transmission mediamay take the form of electric or electromagnetic signals, or acoustic orlight waves such as those generated during radio frequency (RF) andinfrared (IR) data communications. Common forms of computer-readablemedia therefore include for example: a floppy disk, a flexible disk,hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD orDVD-ROM, any other optical medium, punch cards paper tape, any otherphysical storage medium with patterns of holes, a RAM, a ROM, a PROM andEPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wavetransporting data or instructions, cables or links transporting such acarrier wave, or any other medium from which a computer may readprogramming code and/or data. Many of these forms of computer readablemedia may be involved in carrying one or more sequences of one or moreinstructions to a processor for execution.

The computer system 1601 can include or be in communication with anelectronic display 1635 that comprises a user interface (UI) 1640 forproviding, for example, the one or more images of the cell that istransported through the channel of the cell sorting system. In somecases, the computer system 1601 can be configured to provide a livefeedback of the images. Examples of UI's include, without limitation, agraphical user interface (GUI) and web-based user interface.

Methods and systems of the present disclosure can be implemented by wayof one or more algorithms. An algorithm can be implemented by way ofsoftware upon execution by the central processing unit 1605. Thealgorithm can be, for example, a deep learning algorithm to enablesorting of the cell.

Examples

The following specific examples are illustrative and non-limiting. Theexamples described herein reference and provide non-limiting support tothe various embodiments described in the preceding sections.

Example 1. Non-Invasive Prenatal Testing

Sample Preparation:

Five to ten mL of maternal peripheral blood will be collected into anethylene diamine tetraacetic acid (EDTA)-containing tube and a plaintube. For women undergoing amniocentesis, maternal blood will becollected prior to the procedure. Maternal blood samples will beprocessed between 1 to 3 hours following venesection. Blood samples willbe centrifuged at 3000 g and plasma and serum will be carefully removedfrom the EDTA-containing and plain tubes, respectively, and transferredinto plain polypropylene tubes. The plasma or serum samples must remainundisturbed when the buffy coat or the blood clot are removed. Followingremoval of the plasma samples, the red cell pellet and buffy coat willbe saved for processing in the system of the present disclosure.

Sample Testing:

Cells from maternal serum or plasma acquired from a pregnant femalesubject may be imaged using the system and methods of the presentdisclosure, wherein the cells are not labelled and placed in a flow. Thecells will be imaged from different angles. In some aspects, said cellswill be housed in a flow channel within the system of the presentdisclosure, wherein the flow channel has walls formed to space theplurality of cells within a single streamline. In some aspects, saidcells will be housed in a flow channel within the system of the presentdisclosure, wherein the flow channel has walls formed to rotate theplurality of said cells within a single streamline.

While preferred embodiments of the present invention have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. It is not intendedthat the invention be limited by the specific examples provided withinthe specification. While the invention has been described with referenceto the aforementioned specification, the descriptions and illustrationsof the embodiments herein are not meant to be construed in a limitingsense. Numerous variations, changes, and substitutions will now occur tothose skilled in the art without departing from the invention.Furthermore, it shall be understood that all aspects of the inventionare not limited to the specific depictions, configurations or relativeproportions set forth herein which depend upon a variety of conditionsand variables. It should be understood that various alternatives to theembodiments of the invention described herein may be employed inpracticing the invention. It is therefore contemplated that theinvention shall also cover any such alternatives, modifications,variations or equivalents. It is intended that the following claimsdefine the scope of the invention and that methods and structures withinthe scope of these claims and their equivalents be covered thereby.

What is claimed is:
 1. A method for cell sorting, comprising: (a)transporting a cell suspended in a fluid through a flow channel, whereinthe flow channel is in fluid communication with a plurality ofsub-channels; (b) capturing one or more images of the cell from one ormore angles as the cell is transported through the flow channel; (c)analyzing, by a deep learning algorithm, the one or more images of thecell to classify the cell; and (d) sorting the cell to a selectedsub-channel of the plurality of sub-channels based on classification ofthe cell in (c).
 2. The method of claim 1, further comprising rotatingthe cell as the cell is being transported through the flow channel. 3.The method of claim 1, further comprising focusing the cell into astreamline at a height within the flow channel as the cell is beingtransported through the flow channel.
 4. The method of claim 1, whereinthe one or more images are captured at a rate of about 10 frames persecond to about 500,000 frames per second.
 5. The method of claim 1,wherein the one or more angles comprise a plurality of angles thatextend around the cell or over a portion of the cell.
 6. The method ofclaim 1, wherein capturing the one or more images of the cell comprisescapturing a plurality of images from (1) a top side of the cell, (2) abottom side of the cell, (3) a front side of the cell, (4) a rear sideof the cell, (5) a left side of the cell, or (6) a right side of thecell.
 7. The method of claim 1, wherein the plurality of sub-channelsexcluding the selected sub-channel are closed using pressure, anelectric field, a magnetic field, or a combination thereof while thecell is being directed to the selected sub-channel.
 8. The method ofclaim 1, further comprising validating the sorting of the cell using alight.
 9. The method of claim 1, further comprising sorting a pluralityof cells at a rate of at least 10 cells per second, wherein theplurality of cells comprises the cell.
 10. The method of claim 1,further comprising: sorting a plurality of cells including the cellusing a classifier; and feeding data from the sorting back to theclassifier in order to train the classifier for future sorting.
 11. Themethod of claim 10, wherein the classifier comprises a neural networkthat is configured to perform classification of each of the plurality ofcells, based on classification probabilities corresponding to aplurality of analyzed images of the plurality of cells.
 12. The methodof claim 1, wherein the cell is from a biological sample of a subject,and wherein the method further comprises determining a presence or anabsence of a physiological condition or an attribute in the subjectbased on the analyzed image.
 13. The method of claim 1, wherein thecapturing of the one or more images of the cell comprises illuminatingthe cell with a strobe light with an exposure time of less than 1millisecond, wherein the strobe light comprises a wavelength selectedfrom the group consisting of: 470 nanometer (nm), 530 nm, 625 nm, and850 nm.
 14. The method of claim 2, further comprising applying avelocity gradient across the cell to rotate the cell.
 15. The method ofclaim 14, wherein the cell is flown in a first buffer at a firstvelocity, and wherein the applying the velocity gradient across the cellcomprises co-flowing a second buffer at a second velocity.
 16. Themethod of claim 2, wherein an axis of the rotation of the cell and anadditional axis of migration of the cell along the flow channel aredifferent.
 17. The method of claim 16, wherein the axis of the rotationof the cell is perpendicular to the additional axis of the migration ofthe cell along the flow channel.
 18. The method of claim 3, wherein thefocusing of the cell comprises subjecting the cell under an inertiallift force, wherein the inertial lift force is characterized by aReynolds number of greater than
 1. 19. The method of claim 18, whereinthe inertial lift force is characterized by a Reynolds number of atleast
 20. 20. The method of claim 6, wherein capturing the one or moreimages of the cell comprises capturing a plurality of images from atleast two sides selected from the group consisting of: (1) a top side ofthe cell, (2) a bottom side of the cell, (3) a front side of the cell,(4) a rear side of the cell, (5) a left side of the cell, and (6) aright side of the cell.
 21. The method of claim 1, wherein the sortingcomprises (i) directing a first cell to a first sub-channel of theplurality of sub-channels and (ii) directing a second cell to a secondsub-channel of the plurality of sub-channels, wherein the first cell andthe second cell have or are suspected of having one or more differentfeatures.
 22. The method of claim 8, wherein the validating comprisesdetermining information associated with the cell using blockage orscattering of the light.
 23. The method of claim 22, wherein theinformation associated with the cell comprises a size, shape, density,texture, or speed of the cell.
 24. The method of claim 8, wherein thevalidating comprises (i) providing at least two light spots on theselected sub-channel by directing at least two lights towards theselected sub-channel, and (ii) determining a travel time of the cellbetween the at least two light sports, wherein the at least two lightspots are spaced apart by about 10 micrometer to about 1,000 micrometer.25. The method of claim 8, wherein the light comprises a laser.
 26. Themethod of claim 12, wherein the biological sample of the subject isselected from the group consisting of: blood, plasma, serum, urine,perilymph fluid, feces, saliva, semen, amniotic fluid, cerebrospinalfluid, bile, sweat, tears, sputum, synovial fluid, vomit, bone, heart,thymus, artery, blood vessel, lung, muscle, stomach, intestine, liver,pancreas, spleen, kidney, gall bladder, thyroid gland, adrenal gland,mammary gland, ovary, prostate gland, testicle, skin, adipose, eye,brain, infected tissue, diseased tissue, malignant tissue, calcifiedtissue, and healthy tissue, and wherein the malignant tissue comprisestumor, sarcoma, leukemia, or a derivative thereof.
 27. The method ofclaim 1, wherein the flow channel is in fluid communication with theplurality of sub-channels via a sorting junction, the method furthercomprising predicting an arrival time of the cell at the sortingjunction.
 28. The method of claim 27, further comprising: adjusting avelocity of the cell or a subsequent cell through the flow channel basedon the predicted arrival time of the cell, to thereby modify the arrivaltime of the cell or an arrival time of the subsequent cell at thesorting junction; and sorting the cell or the subsequent cell to theselected sub-channel of the plurality of sub-channels.
 29. The method ofclaim 12, wherein the biological sample comprises maternal blood orserum.
 30. The method of claim 29, further comprising identifying thecell as a nucleated red blood cell (RBC), wherein presence of thenucleated RBC is indicative of a fetal abnormal condition, which fetalabnormal condition comprising fetal aneuploidy.